Working in AI is a little like a foot race on a tightrope right now. Between the surge in computing costs for engineering teams to the unique challenges that come with building agentic systems, AI engineers have to run faster and more precisely than ever — and every AI team has to find their stride if they want to be successful. The following AI teams, however, have some tips when it comes to applying emerging technologies to product, workflow and customer challenges while keeping quality and responsibility in focus.
Take the engineering team at Navan, a corporate travel and expense software company, for example.
“Working on the AI team means architecting for extreme velocity,” Nir Benjano, an AI architect, said. “The environment is fast-paced and constantly shifting, driven by rapid prototyping through vibe coding, rigorous evals and single-turn LLM testing.”
Meanwhile, the engineering team at cloud storage company Dropbox focuses on experimentation as the path to building future-focused products.
“Engineers are encouraged to challenge assumptions, measure outcomes and share ideas openly,” Sean-Michael Lewis, principal engineer said. “More importantly, you are encouraged to just try to build the thing you think will matter.”
Built In spoke with more than a dozen engineers who shared how their team is using AI to build emerging tech while scaling for success.
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iManage develops an intelligent, cloud-enabled, secure knowledge work platform.
What’s it like to work on the AI and machine learning team at your company?
It is highly collaborative, learning-oriented and focused on continuous improvement. We work closely with product and engineering stakeholders, including knowledge engineering, legal experts, backend and frontend engineers and SREs.
A big part of the work is understanding how AI models and applications fit into the broader system, developing a deep understanding of customer pain points and engineering constraints, to enable us to design and develop business-meaningful and trustworthy AI solutions.
The team encourages curiosity, continuous learning and growth. We strongly encourage exploring new ideas, experimenting with emerging technologies and sharing knowledge; and we back that up with a high tolerance for failure. This culture of rapid experimentation is how we turn the best ideas into real, reliable solutions for our customers.
How is your team applying emerging technology in practical, business-relevant ways?
We apply AI to concrete legal document management workflows, working closely with product and legal expert teams to evaluate different use cases, from traditional machine learning to cutting-edge generative AI and agentic applications, such as document classification and enrichment and agentic workflow automation. The goal is not to use AI because of the hype, but to solve real business problems and improve customer workflows.
We stay on top of AI research and evaluate which ideas can actually become useful products. There is a strong experimentation culture around moving from research idea, to prototype, to evaluation and eventually to production when the idea proves valuable.
We also care deeply about AI system efficiency and scalability. Even in the agentic AI era, "garbage in, garbage out" still applies and arguably even more so. At iManage's scale we need both strong model quality and good latency, throughput and cost efficiency.
We are also bullish on leveraging AI-assisted coding tools, such as Claude Code. We actively learn and share best practices to use them in a reliable and verifiable way, rather than simply chasing "token-maxxing."
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Candidates should know that the work spans the full AI stack: data curation, model training or fine-tuning, evaluation, LLM inference, agentic system development, backend integration, deployment and production monitoring.
We value people with both breadth and depth. The best candidates are curious full-stack generalists who can reason across the system while also going deep in one or two areas such as LLM architecture, inference optimization, evaluation, or applied ML engineering.
Collaboration and communication skills are very important. AI work here involves many cross-functional partners, so being able to explain ideas, solutions and tradeoffs clearly is just as important as technical depth. Clear context and a good understanding of the "why" behind decisions makes the work feel more meaningful and grounded. We tend to see that people who think beyond technical silos and combine strong technical skills with solid product sense also make the best technical bets.
SailPoint is an identity security company that uses the power of AI and machine learning to empower organizations to manage and secure access to applications and data.
What’s it like to work on the AI and machine learning team at your company?
Working on the AI and machine learning team here is, in a word, thrilling. We aren’t just building standalone AI features; we are actively shaping how our company operates. Our mission is to rethink how value is created at work, developing new workflows using the latest emerging technologies. AI touches every part of the business and our team operates cross-functionally, collaborating closely with teams across the organization.
Our guiding philosophy is to eliminate mundane, repetitive tasks from our colleagues’ daily work. By automating routine work and handling cognitive heavy lifting, we help teams focus on strategic thinking, creativity and deep problem-solving. It’s a highly rewarding environment where you can directly see the impact of your work. You’re not just building agents — you’re improving the human experience at work.
How is your team applying emerging technology in practical, business-relevant ways?
Our approach is structured, practical and grounded in tangible business value — not hype. We don’t implement AI for its own sake. Instead, we partner with business leaders to design a customized AI Transition Operational Plan tailored to each department. This playbook works because it prioritizes business outcomes first. Once those outcomes are defined, we map the necessary transition tasks. A key focus is “agentification” of workflows — the intelligent evolution of traditional automation.
Beyond delivering technology, we define clear enablement metrics. We believe in not just doing the work, but equipping teams to do it themselves — building digital dexterity and long-term self-sufficiency. We also support leadership in estimating budgets and deciding whether to build or buy tools to meet those goals. While this playbook is effective, we stay grounded in the reality that the hardest — and most critical — part is orchestrating disparate datasets to meet the significant information demands of advanced agentic workflows.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Candidates should know that the AI landscape evolves at a breakneck pace and specific tools are always changing. As a result, being a fast, adaptable learner matters far more than expertise in today’s tech stack. You must be comfortable continuously learning from a wide circle of peers. Because our work spans the entire business, you also need to be a versatile communicator — able to translate complex AI concepts into clear, practical terms for different functions.
Most importantly, candidates need to understand how we view the intersection of technology and humanity. We view AI not as a replacement for our people, but as a force multiplier — an essential tool that empowers them to achieve more, faster. Finally, humility is essential. No one is a definitive expert in this space — we are all learning together in a supportive, ego-free environment.
Navan is a corporate travel and expense software company that makes travel and expense easy.
What’s it like to work on the AI and machine learning team at your company?
Working on the AI team means architecting for extreme velocity. The environment is fast-paced and constantly shifting, driven by rapid prototyping through vibe coding, rigorous evals and single-turn LLM testing. My role is to ensure our technical foundation is flexible enough to support this pace. We routinely refactor, rebuild and ship in days instead of months — relying on adaptable design to turn that speed into a reliable engineering advantage.
How is your team applying emerging technology in practical, business-relevant ways?
At Navan, practical AI adoption means moving from theoretical breakthroughs to operational value very quickly. The architecture is designed to seamlessly integrate new models, techniques and design patterns the moment they emerge. And we leverage AI across the entire engineering and operational lifecycle — from automated code reviews and dev workflows to real-time system monitoring and automated support. Because the technical foundation is built to be modular and adaptive, new tech can be ingested at a speed that directly accelerates the business.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Candidates need to know that Navan is an intensely fast-paced company where things change very often and joining this team comes with exceptionally high expectations. Our tech stack is highly pragmatic; we leverage LangChain and all the major LLM providers, writing agents in both TypeScript and Python, alongside our own internal Navan Cognition platform.
With the technology and priorities shifting so quickly, it’s impossible to keep track of everything manually. Instead, we rely on automated validation to maintain code quality, stepping into design meetings only when needed. We move with high autonomy and deploy using feature flags, giving us the safety net to ship fast and instantly disable a feature if a bug occurs.
To thrive here, you have to be comfortable with constant evolution and be able to “think like the LLM” to diagnose prompt conflicts and model failures. We look for open-minded, zero-ego and data-driven engineers who can handle the pace. We move fast, but we never compromise on the final output — flawless, high-quality delivery is our absolute priority.
Lessen is a tech‑enabled, end‑to‑end property service provider trying to change how commercial and residential real estate services are managed at scale through a data‑driven platform and a vetted network of vendor partners.
What’s it like to work on the AI and machine learning team at your company?
Li: Our work is product-focused, which not only contains building agents or training models, but also to understand the true pain points from customers, improve our day-to-day work solutions and provide an efficient and trustful approach for the team. AI is the golden tool while data is our key to success. We work with top-notch members in each team from Lessen to listen to their needs, simplify complexity, help them convert big, creative ideas into accurate, explainable, simplified project outputs for our real-world workflows to win together as a team.
Scott: Working with the AI and machine learning team at Lessen is exciting because the work is directly connected to real operational problems. We are not exploring AI in theory. We are applying it to workflows that impact clients, residents, vendors and internal teams every day.
One of the things that makes the team successful is the level of transparency and collaboration involved in the work. AI products can change quickly as we learn more, so we are constantly sharing progress, reviewing outputs and inviting feedback from the teams closest to the problem. That openness is important because it keeps us focused on building solutions people can trust and actually use.
The best part of the work is the balance between creativity and accountability. We get to think big about what AI can become, but we also have to make sure what we build is accurate, explainable, reliable and useful in production. It is a fast-moving environment, but the goal is always the same — solve real problems in a way that creates measurable value for the business and a better experience for the people using our products.
How is your team applying emerging technology in practical, business-relevant ways?
Li: By looking for areas where manual work can be reduced, response quality can be improved and our workflow can be supported more smoothly for both internal teams and customers. For example, we use new AI techs to drive meaningful changes: Help internal teams summarize customer needs through communications, interpret customer interactions to act faster and extract important information for better decision-making. Emerging technologies become valuable when connected to clear business outcomes.
Scott: Our approach is to connect emerging technology to clear business outcomes. AI becomes valuable when it helps someone make a better decision, complete work faster, improve the customer experience, or reduce manual effort.
A good example is how we are using AI to support work order creation and management. Instead of asking users to understand complex operational rules, AI can help interpret the request, ask relevant follow-up questions, identify the right service need and guide the process toward a better outcome. We are also applying AI across communication workflows, summarization, decision support, issue detection and automation so teams can act faster with better context.
The practical value comes from how closely we connect the technology to the actual workflow. We do not treat the first version of an AI solution as the final answer. We test it, review it with the teams using it, listen to what is working or not working and adjust quickly. That constant refinement can create extra work, but it is also what helps turn emerging technology into products that are useful, trusted and aligned to real business needs.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Li: I believe the candidates should have both a good technical background as well as a good understanding of the business/product requirements. This enables them to understand the scope of work, ask the right questions, so they can quickly turn good proof of concept demos into production ready projects. We need to "think big, align deeply, build precisely." so, we can explore AI opportunities, understand the business and execute and take care of what we build precisely from beginning to end to deliver and hold ourselves accountable to our customers.
Scott: Candidates should know that AI work at Lessen is highly collaborative, fast-moving and very transparent. The tools and technology are important, but the real differentiator is how closely the AI team works with product, engineering, operations, client-facing teams and business leaders to make sure we are solving the right problems.
One of the most important parts of working on AI products is being open to feedback and willing to adjust quickly. We are not building in isolation and waiting for a long feedback cycle. We are constantly sharing progress, reviewing outputs, pressure-testing assumptions and making changes as we learn more. That can create additional work at times, but it also helps ensure the final solution is practical, trusted and valuable to the people using it.
Candidates should be comfortable working in that type of environment. They need to be curious, flexible and willing to challenge their own ideas. Successful AI work requires more than building something impressive in a demo. It requires understanding the workflow, listening to the teams closest to the problem, being transparent about limitations and continuing to refine the solution until it creates real business value.
Kustomer’s AI-native CX platform is designed to help businesses meet the needs of customers.
What’s it like to work on the AI and machine learning team at your company?
Our entire company is an AI team! We focus on using AI for speed and collaboration that’s grounded in real customer problems. We ship AI features that CX teams use every day, like Data Explorer for natural language analytics and our Architect, Envoy and Concierge, so the feedback loop is immediate and the work never feels academic. I get to partner closely with engineers, designers and customers to turn emerging capabilities into products that make a measurable difference.
How is your team applying emerging technology in practical, business-relevant ways?
AI is at the core of customer service operations for both B2C and B2B customers. Data Explorer lets CX leaders ask questions of their data in plain English, while Architect, Envoy and Concierge handle reasoning, routing and resolution across conversations. The focus is always on practical outcomes like faster resolutions and better insights and we obsess over trust and accuracy because these tools have to earn their place in a customer support rep's daily workflow.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Expect to work hands-on with frontier LLMs and to think hard about how AI behaves in real production environments where reliability matters. Collaboration is constant and cross-functional and the most valuable skill is translating ambiguous customer needs into clear product decisions. AI has enabled our product to meet customer needs at the fastest pace ever. If you like solving problems where the technology is genuinely new and the impact is immediate, you'll feel at home here.
SmartBear provides a portfolio of trusted tools that give software development teams around the world visibility into end-to-end quality through test management and automation, API development lifecycle and application stability.
What’s it like to work on the AI and machine learning team at your company?
The AI/ML team at SmartBear is focused on building BearQ — our agentic QA platform. The core problem we're solving is that AI has fundamentally changed how applications are built and consumed and traditional testing approaches can't keep pace. Our team's job is to close that gap at AI speed and scale.
Because of that, the work is genuinely broad. From the product side, I’m between two pieces of the work: shipping new product features and the ongoing improvement of how the agents behave based on what we and our customers see in production. Most weeks, I’m moving between engineering decisions, customer conversations and the patterns surfacing in real usage.
The team is organized as cross-functional pods — product management, design and engineering working on the same problem at the same time. We ship small, watch what the agents do in real environments and adjust from there. We plan for the future but are highly dynamic. Multi-year roadmaps don’t hold up when you’re working with systems that behave unpredictably, so we plan and execute in shorter windows.
How is your team applying emerging technology in practical, business-relevant ways?
BearQ is an agentic quality assurance platform built on LLMs and runs three coordinated agents against a web application. There’s an explorer agent that maps the app’s UI and state transitions, a tester agent that validates user flows end-to-end and a QA lead agent that coordinates them. The aim is to remove the brittleness and bottlenecks that have held back automated UI testing — a problem AI coding velocity makes harder to ignore.
Real web apps are messy. Auth flows, modals, race conditions, components that look the same but behave differently depending on state, etc. A significant part of our engineering work is getting agents to act reliably in that environment and just as importantly, knowing when to surface decisions to a human rather than proceed autonomously. We're intentional about where LLM judgment adds value versus where deterministic, auditable outcomes are required.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
On tools: The team uses foundation models from the major providers and has built a meaningful amount of in-house agent orchestration. The model choice is constantly evolving to balance performance, speed and cost. Most of the interesting work, though, sits around the model rather than in it: orchestration, prompt design, agent coordination and the product surfaces that let us and our customers catch when an agent has gone off the rails.
On collaboration: As mentioned, we rely on a cross-functional pod structure where product managers, design and engineering work on the same problem at the same time. The team tends to learn more from running something than from spec’ing it out in advance, so we lean toward shipping early and tuning live.
On problem-solving: The quality assurance domain rewards people who take it seriously. You don’t need to be a quality assurance expert on day one, but understanding how real testers think about their work tends to matter more than the AI specifics. The work also asks for comfort with ambiguity. There aren’t widely accepted answers yet for how to build dependable agentic systems, so part of the job is forming an opinion and being willing to revise it.
Genius Sports is the sports technology company behind GeniusIQ, a big data and artificial intelligence platform that ingests and computes real-time data feeds to measure and analyze sports performance and officiation, fan engagement, advertising, sports betting and more.
What’s it like to work on the AI and machine learning team at your company?
As a software engineer on the Soccer AI team my work is centered around GeniusIQ, our next-generation data and AI platform.
In particular, I focus on data generation, or what we call soccer semantics: turning the raw materials from our high-resolution event and tracking data into real soccer meaning.
I have the opportunity to work with what I believe is the most detailed, advanced tracking and event data in the industry. With this, we work on some of the most interesting and challenging problems in the field for the biggest leagues and teams in the world.
In my role, new modeling development is especially interesting. My process heavily utilizes historical games and exploring the results of a new run within the context of Performance Studio, our data and video platform for top-level video analysis.
Typically, I'll have code on one monitor and a constant stream of match footage on another. That's something I certainly wouldn't do in another job!
How is your team applying emerging technology in practical, business-relevant ways?
Our goal is to be the "operating system of modern sport," so our work is naturally wide-ranging in scope: from team performance, to media, broadcast and betting.
There are over 3 million center-of-mass tracking data points in a single game and our skeletal tracking increases this by an order of magnitude. From that data, we provide detailed physical metrics for all players and referees throughout each game.
These have numerous applications powered by GeniusIQ, from player performance, to fantasy games and even evaluating refereeing performance. The possibilities are limitless and our software needs to be highly accurate and flexible to extend to any number of new use cases.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Our systems are expected to run live at low latency with high accuracy, all integrated into a large, distributed architecture. That means stream processing is a key part of our work. We manage race conditions, asynchronous updates and state management across distributed workers, all at low latency.
Cooperation is massively encouraged here. Every team member brings their own unique skills and interests and some of the most interesting ideas lie in the connections between seemingly unrelated domains. We've drawn ideas from other sports, chess engines and even cancer biology. We're encouraged to take initiative and explore problems that we find important.
Most importantly, our teams are composed of not only talented, but passionate engineers. It's hard not to be passionate about working in sport and Genius is full of people who take initiative in driving not only our company's technology forward, but also the field as a whole.
Ahold Delhaize USA is a marketing and adtech company that’s a division of global food retailer Ahold Delhaize and leading omnichannel grocery brands.
What’s it like to work on the AI and machine learning team at your company?
It has been very fulfilling to work and lead AI and machine learning engineering teams at Ahold Delhaize USA. We have a unique set of skillful individuals across the AI/ML engineering team, bringing strong expertise that enables us to develop and scale AI and machine learning capabilities across our business. While we are still in the early stages of our AI journey, we have significantly increased our AI learning, adoption and service capabilities over the past three years. I have been impressed by Ahold Delhaize USA’s willingness to invest in and explore advancing toward a more AI-enabled grocery business. I think that mindset and commitment will position us well for long-term success.
How is your team applying emerging technology in practical, business-relevant ways?
Over the course of my career, I have worked for three Fortune 100 companies who were all best-in-class in how technology was invested and used to drive strong business outcomes. Ahold Delhaize USA is practicing that same methodology when it comes to how we are investing in our technology stack to advance the business forward. Our data and AI/ML teams quickly evaluate emerging data and AI/ML technologies and assess existing tools to make more informed investment decisions. This approach has helped increase AI adoption, accelerate the speed of data and AI/ML services and strengthen our overall security and governance posture for better protection of data and AI assets. As a result, we’ve seen an increase of over 140 percent utilization growth on one of our platforms over the past three years. This demonstrates the trust the company continues to place in our data and AI/ML platforms for servicing business needs.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
I believe the best environments for AI work are those companies that remain curious, constantly assess new ideas and have an investor mindset to allow creativity and innovation. At Ahold Delhaize USA, we foster that mindset and have positioned ourselves to embrace the AI wave as an opportunity to accelerate productivity, ensure quality control, optimize spending, enhance decision-making and most importantly — elevate the shopping experience. It’s an excellent time to be a part of grocery tech. Ahold Delhaize USA has established itself as a leader focused on applying AI in ways that deliver meaningful value and welcomes talented individuals who want to work on impactful, real-world business challenges.
Dropbox provides cloud-based solutions for file storage, sharing and collaboration, leveraging AI to transform knowledge work for over 700 million users.
What’s it like to work on the AI and machine learning team at your company?
My recent AI work focuses on experimenting with running and operating LLMs on our own hardware. This goes beyond using AI to code, but involves understanding how the LLMs work and how to extract the best performance from the hardware for the nuances of each model.
Working on AI at Dropbox means solving real product problems, not building demos. Our team combines research with production engineering to create AI that helps people find information, make decisions and get work done more efficiently. At Dropbox, we’ve evolved from traditional retrieval systems to agentic AI that can reason, plan and take action across a user’s work. It’s a highly collaborative environment where ML engineers, infrastructure engineers and product teams iterate together, constantly balancing model capability, performance, quality and user trust as AI moves from experimentation into everyday workflows.
How is your team applying emerging technology in practical, business-relevant ways?
We have found that coding agents are useful for a wide range of tasks outside of coding. The sandboxing needed to provide safe autonomous coding agents translates directly into the necessary technology to provide more user-friendly agents for our customers.
One area we’re focused on is context engineering — ensuring AI has the right information at the right time to make better decisions. As Dropbox powered by Dash has become more capable, we’ve learned that simply giving models more context isn’t the answer. Instead, we consolidate retrieval, filter information aggressively and use specialized agents for complex tasks. These architectural decisions improve accuracy, speed and cost while enabling AI to search, summarize and act across the tools people already use every day. It’s a practical example of turning emerging AI techniques into reliable product experiences.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Engineering is constantly developing new agent skills and runtimes to help engineers get work done. What started as a skill to monitor your pull requests has turned into agents that address comments automatically. With each new shift in how we use AI, we integrate it more deeply into our development lifecycle. As we improve our own development flows internally, what we learn informs how we build our products.
Building production AI requires close collaboration across machine learning, backend infrastructure, search, product and design. We are considering user experience in all of our work, from the front end chrome down to the GPU. Engineers are encouraged to challenge assumptions, measure outcomes and share ideas openly. More importantly, you are encouraged to just try to build the thing you think will matter. The problems are technically deep — from designing agent architectures to optimizing context and performance — but they’re always grounded in delivering useful, trustworthy AI for customers.
Rula is a mental healthcare tech company.
What’s it like to work on the AI and machine learning team at your company?
Although Rula is a large, established behavioral healthcare platform, the AI engineering team is still in its early stages. This means we get the best of both worlds: the stability, resources and reach of a firmly established organization, along with the energy, speed and greenfield opportunity space of an AI startup. In practice, we get to build truly innovative and impactful AI products that measurably improve the experiences of behavioral healthcare providers and patients at scale.
How is your team applying emerging technology in practical, business-relevant ways?
The AI engineering team at Rula is building AI-powered product experiences that enhance behavioral healthcare for both patients and providers. Our work spans the full therapy journey: from helping patients find the right provider, to assisting clinicians with pre-session preparation, to providing patients with AI-powered support between therapy sessions. Rather than replacing the human connection at the core of therapy, we use AI to empower patients and providers with timely, personalized support throughout the care journey.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
This is an incredibly exciting time to be working at the intersection of AI and behavioral healthcare. At Rula, you’ll work with mission-aligned teammates on collaborative, cross-functional projects, in a fast-moving, startup-like environment. The work you do here has the potential to improve the daily experience of tens of thousands of people every single day. If you’re excited about building with cutting-edge AI tools and delivering meaningful impact in people’s lives, Rula offers a fantastic opportunity to do both.
AirDNA is a provider of data and business intelligence for the billion-dollar travel and vacation rental industry.
What’s it like to work on the AI and machine learning team at your company?
Working on the AI and ML team here is a fun challenge and a lot of that comes down to the nature of our data. We’re not pulling clean rows out of a database. We’re scraping the short-term rental market across Airbnb, VRBO and Booking.com and each platform has its own quirks, gaps and ways of changing overnight. A listing’s status can flip, a property can disappear, prices can spike for reasons that have nothing to do with the market.
That means a big part of the work isn’t just modeling. It’s figuring out what the data is telling us and then deciding what to trust. Almost every problem starts as a question about the data before it becomes a question about the model.
The modeling side is where the variety kicks in. On any given week we might train a classification model to infer a listing’s status, work through pretty much every flavor of regression to forecast demand or pricing and turn around and use state-of-the-art LLMs on a completely different project. No single tool fits. The problem dictates the approach and that’s what keeps it fun.
How is your team applying emerging technology in practical, business-relevant ways?
Our approach to emerging tech is pretty simple. We don’t jump on the hype train just because something is new. There’s no shortage of cool tools out there, but cool doesn’t always mean useful and useful doesn’t always mean useful for us.
Before we pick up a new technology, we take the time to understand it. What problem does it solve? Where would it fit in our stack? Is it solving something we already have, or are we inventing a problem to justify the tool? Those questions sound obvious, but skipping them is how teams end up with shiny things that don’t move the needle.
When something does pass that bar, we lean in. We’ve integrated LLMs into projects where they genuinely outperform what we had before and we’ve passed on plenty of other ideas where the math didn’t work out. The goal is always the same; figure out where emerging tech can give us a real edge on the data and problems that are unique to short-term rentals. That’s where differentiation comes from, not from being early for the sake of being early.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
The biggest thing for candidates to know is that every idea is welcome here. Whether it comes from someone with ten years of ML experience or someone who just joined, we’d rather hear the idea and figure out together if it has legs than miss it because the room wasn’t open to it.
A lot of our current AI work is focused on automating processes that have historically been very manual. When your data is messy and your workflows have grown organically over time, there’s plenty of room to make people’s days easier and free them up for higher-leverage work. That’s where a lot of our energy is going.
What ties it all together is collaboration. Engineering, product and data each see a different piece of the puzzle and the best ideas usually come out of those conversations rather than from any one person sitting alone with a model. The candidates who do well here are the ones who like that back-and-forth and who care as much about whether something is useful as whether it’s interesting.
FloQast offers an accounting platform that enhances the way accounting teams already work to help them operate more efficiently.
What’s it like to work on the AI and machine learning team at your company?
I feel lucky to work on a team of smart and driven engineers, developing the AI foundation for FloQast. Engineers on this team have complete ownership over their area, and right now, we’re hard at work on improvements to our core AI tool, FloQast Transform, that will provide considerably more value to the end user. It’s exciting solving the problems that will shape how this company builds AI for years to come.
How is your team applying emerging technology in practical, business-relevant ways?
Our best example is Transform AI agents, which lets FloQast users use AI to build deterministic workflows that transform their data and automate the tedious, recurring stuff that eats up their day. And since our customers are accountants, accuracy and auditability are paramount. The AI has to do exactly what they'd have done and show how it did it or they won't trust it.
What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?
Our AI engineers spend real time with actual accountants, our customers, to understand the work before we ever start building. That partnership is exactly why we built our eval harness, which tests every change we make to our agents before it ever reaches a customer. We care as much about getting it right as we do about building fast.
