Want to work somewhere that’s pushing the boundaries of technology while still maintaining a close-knit team culture? Look no further. The following engineering teams are adopting AI, automation and modern frameworks to build faster and smarter, while fostering collaboration and learning.
Just ask Ashley VanderWel, vice president of engineering at pet health and nutrition company The Farmer’s Dog. According to VanderWel, her employer sees engineers as product strategy guides — not just mechanics.
“We bring engineers into conversations early, encourage them to challenge assumptions, contribute ideas and help shape the direction we’re headed,” VanderWel said.
Meanwhile, at audience insights platform DISQO, an open culture means anyone can bring a new tool or idea forward.
“The bar isn’t whether something is new or exciting, but whether it solves a real pain point, makes a process meaningfully faster, or replaces a process entirely,” Nej Kutcharian, senior director of software engineering at DISQO said.
If you’re looking for an innovative, collaborative team that values autonomy and cutting-edge tools, these nine hiring companies are worth considering. But don’t take our word for it — listen to what their engineers had to say about team culture.
Featured Companies
SharkNinja is an e-commerce company that brings everything from beauty products to robotics to market.
How do engineers influence innovation and product strategy?
Engineers shape innovation and product strategy from two directions. The first path starts with the consumer. A consumer problem is identified and engineering delivers a feasible solution. This is harder than it sounds, as the best products are often those that people didn’t know they wanted. So while problems may sound obvious and easy to find, they are often accepted and therefore go unrecognized. The second path runs in the opposite direction. Engineering curiosity, exploration and endless tinkering produce a novel technical insight and then we work backward to determine if and where this can positively impact the consumer. This approach has driven the evolution of some of our most innovative product lines, solving problems previously believed to be unsolvable.
In both cases, engineering is an integral part of innovation and product strategy. While ideas for a product may come from different places, it is engineering that makes them real.
How do you decide when to adopt new tech or tools?
The problems we are solving are constantly changing, so the tools we use must also change.
At some companies, development is limited to the known and the previously achieved, which often means saying no to new ideas. SharkNinja cannot exist like that because we are continually solving new consumer problems, which means we cannot accept the status quo. When we identify a potential path that would be new or challenging, we do not start with “no.” We start with “how might we?” which enables a conversation focused on achieving. A new approach may be hard and it may be unproven, but that is not a reason to avoid it. Once the barrier of newness and the fear of failure are removed, anything becomes possible. In my opinion, this is the most fun environment for an engineer to operate within.
This mindset shapes how we adopt new technology and tools. Because the problems we are solving are always new, the tools must also be new. And because we work at a very fast pace, we make sure to adopt the tools before they are needed to ensure it is not too late.
What practices help engineers balance experimentation and reliability?
Early in a project we’re in the exploration phase when we’re still learning about the product and maximizing capabilities. Later in the schedule we get more focused on quality and schedule. But even in these later stages when we’re hyperfocused on delivering, we remember that we’re not just delivering today but also in the future. That means we need to continue to build our pipeline which is heavily driven by innovation. By allowing a portion of us to pursue those tangent explorations, we may have a breakthrough.
Throughout a project development cycle, we’re constantly learning. But learning one thing typically leads to more questions than answers. So there’s an endless number of questions we could chase. The hard part is knowing which ones are worthwhile, as we don’t have infinite time and resources. Design for Six Sigma principles are utilized to assess feasibility of long term success. Along with how exciting a finding may be, we can gauge if a path is worth exploring. This approach ensures our resources remain deployed against the highest ROI workstreams.
It has led to some extraordinary findings in parallel with the team delivering high quality products on time.
Capital One, is a bank that thinks and works like a tech company, using digital fluency to improve the customer experience.
How do engineers influence innovation and product strategy?
At Capital One, engineers are strategic partners, not just builders. When modernizing the technical system behind Capital One auto’s car-buying experience, my engineering pod partnered closely with the product team from early design all the way through delivery. We were given the autonomy to choose the right tools and design key parts of the flow. By having a seat at the table early on, we designed a cloud-based flow that reduced processing times from hours to under an hour — directly improving how quickly customers can shop for vehicles.
How do you decide when to adopt new tech or tools?
We decide to adopt new technologies when our existing infrastructure needs to scale, speed up or better serve the end user. When we set out to rebuild a vital data pipeline, we moved to a modern, cloud-native stack. What’s great about Capital One’s culture is that adopting new tech comes with built-in support to learn it. I was encouraged to upskill on the job and earn my AWS certification while building out the new environment. We adopt tools that give us the foundation to deliver scalable, long-term impact.
What practices help engineers balance experimentation and reliability?
Finding that balance comes down to pairing creative problem-solving with rock-solid engineering fundamentals. We’re given the space to whiteboard and experiment with new ideas, but we anchor everything we ship in version control, rigorous testing frameworks and constructive code reviews. Working in collaborative engineering pods allows us to move fast and innovate nimbly while ensuring every piece of code is thoroughly vetted for stability and reliability before reaching production.
DISQO is an audience insights platform that provides advertising intelligence, measuring brand and performance outcomes across every media channel to power data-driven marketing decisions.
How do engineers influence innovation and product strategy?
At DISQO, engineers don’t wait for a finished spec. They sit down with the people who own a problem to understand the real challenge and the job to be done.
When asked to optimize a step, our engineers often ask whether the step should exist at all. With AI changing how problems get solved, we try not to be bound by how things worked in the past.
For example, one of our key brand metrics measures whether people remember a brand without being prompted. It required manual setup for every campaign. Our engineers prototyped an AI-driven approach that needed no extra inputs at all, ran it across dozens of real campaigns and showed it was more accurate than the existing method. It’s now being built into our self-service platform, where simple setup matters most.
By rapidly turning ideas like this into working prototypes, our engineers can prove a strategic concept in hours. A demo makes the case faster than a slide deck. Early on, nothing is off the table technically; guardrails come later, when we review the prototype and technical design. Open exploration first, disciplined evaluation second. That’s how engineers shape strategy, not just execute it.
How do you decide when to adopt new tech or tools?
DISQO has an open culture when it comes to new tools and technology. Anyone can bring one forward. The bar isn’t whether something is new or exciting, but whether it solves a real pain point, makes a process meaningfully faster, or replaces a process entirely.
The person proposing a tool owns the first step: building a proof of concept that shows it working against a real problem. They then bring it to Bar Raisers, a weekly meeting open to all of engineering and product, where anyone can present something they’ve found, built, or are thinking about and get candid feedback. If the group comes away confident, the tool moves to a deeper evaluation.
This keeps the barrier to experimentation low, so good ideas can come from anywhere, while grounding adoption decisions in evidence rather than hype. Just as important, Bar Raisers break down silos, so every evaluation becomes a chance for the whole organization to learn, grow and experiment together.
What practices help engineers balance experimentation and reliability?
At DISQO, we don’t see experimentation and reliability as a tradeoff. Strong reliability practices are what let us experiment with confidence. Quality extends from our code to everything we put in front of clients, so we integrate it from the start.
That starts with how we ship. New work goes out behind feature flags and rolls out progressively, so we learn from real usage before anything reaches everyone. SLOs and error budgets turn reliability into a shared, measurable target, so teams know when there’s room to push and when to invest in stability. And blameless postmortems mean every lesson makes the next experiment safer.
Nowhere does this balance matter more than with AI. We want to try new approaches as soon as they’re useful, but because of the dynamic nature of these systems, traditional deterministic tests aren’t enough. We have built robust, custom evaluation frameworks into our definition of done to programmatically verify that all outputs are factual, traceable to verified data sources and highly reliable. In production, we continuously monitor performance to ensure consistency. That’s what lets us move fast with emerging tech without compromising trust.
Onebrief is a software company for military planning.
How do engineers influence innovation and product strategy?
The biggest thing is customer feedback. It realigns what you’re doing and you stop guessing. Everyone says they want it. The difficulty is that talking to a customer might tell you that you’re wrong. You have to be willing to hear that.
At Onebrief, we talk to users as widely and deeply as possible, including going into their environments. We also have dedicated customer teams, so what they learn gets back to product owners and engineers in a fast loop. Engineers can then help decide what we should build, not just how to build it.
A recent example was replacing a loading bar with descriptive steps that explain what the software is doing. The work takes the same amount of time, but the experience feels faster and more human.
That’s a small change, but an important point. Innovation doesn’t always mean adding something bigger. Sometimes it means seeing the same problem from the customer’s side and building something they can understand.
How do you decide when to adopt new tech or tools?
It comes down to cost versus benefit. Does this actually make something easier, or have we just added another thing to manage? We’re constantly trying new tools because you have to use them to find out where that balance is.
A recent personal example is ChatGPT. I had it find my kids’ back-to-school list in my email, then use a browser to put the supplies into a Walmart basket. It went from finding the information to doing something useful with it.
That’s what interests me about connecting AI to the services we already use. Answering a question is useful. Taking work off your hands is a different level of useful.
I drive platform-wide agentic solutions at Onebrief and that’s how I look at new technology. What job can it help someone get done? How much effort does it remove and how much does it introduce? Being new is a reason to try something. It isn’t, by itself, a reason to adopt it.
What practices help engineers balance experimentation and reliability?
Testing the software matters. So does testing your assumptions about the people using it. At Onebrief, we involve customers as early as possible, so their feedback helps shape what actually gets built.
People get used to their software. They learn where things are and how to get their work done. Change that environment and you’ve given them another job: learning it again. Even a change that looks better to an engineer can make life harder for the person using it.
That’s why I think new products or separate experiences are often the best place to try a fundamentally different approach. You can explore without constantly rearranging something people already depend on.
It’s important to get the experience right the first time. That doesn’t mean guessing until you think it’s perfect: It means getting feedback while the design is still easy to change, rather than making customers live with your guesses.
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Ro is a direct-to-patient healthcare company with a mission of helping patients manage their care journey.
How do engineers influence innovation and product strategy?
At Ro, engineers are product partners, not just implementers. They actively shape product strategy by identifying architectural challenges, surfacing recurring pain points and uncovering opportunities to build new capabilities.
One example is our work on a unified health data record. Our engineers recognized that patient health data spread across multiple microservices created technical complexity and made it harder to deliver a connected patient experience. Addressing this challenge led to a reusable foundation for health data that can support new capabilities, including clinical interpretation and alerting. What began as an architectural problem became an opportunity to improve the patient experience and unlock new product capabilities.
Engineers also help eliminate operational bottlenecks. By using tools like centralized Claude skills, they’ve automated parts of the workflow for updating structured health records alongside patient-facing online visits, reducing manual coordination between clinical and engineering teams.
This proximity to our products and workflows allows engineers to turn technical insights into scalable solutions that benefit both patients and the business.
How do you decide when to adopt new tech or tools?
Before evaluating a new tool, we need to understand what problem we’re trying to solve and why it matters. Our primary lens for adopting new tech is whether it solves that problem and improves the patient experience. Ro also creates opportunities for engineers to experiment with emerging technologies. A recent example was an engineering-wide hackathon centered around Claude. Engineers identified bottlenecks in our workflows, then prototyped ways LLM-enabled tools could address them.
One problem was time spent on repetitive “housekeeping” work, like updating dependencies. This experimentation led to Zero, our coding agent. By taking repetitive work off engineers’ plates, Zero frees them to focus on complex technical challenges and building products that improve patient care. Another area was validation and user acceptance testing. Engineers built a Claude-enabled tool to generate test patient scenarios more efficiently, shortening feedback loops and helping us ship high-quality features faster.
This is how we approach adopting new technology at Ro — start with a real problem, create space to experiment, prove tangible value and invest further when it demonstrates how it can make our teams or products
What practices help engineers balance experimentation and reliability?
At Ro, experimentation and reliability aren’t treated as opposing goals. We design our processes so teams can test ideas quickly without compromising the quality of the patient experience.
A crucial part of this is making incremental changes and adding telemetry. For example, we may release feature-flagged experiments aimed at improving the patient journey, such as streamlining the insurance approval process. These experiments include telemetry at the data, application and UX layers, allowing us to understand whether an idea works and how it impacts patients. We use that insight to iterate before broader adoption.
We apply the same philosophy to our software development workflows. When introducing tools like code review agents, we don’t immediately roll them out across every engineer and pull requests. We start with a smaller cohort and narrower use cases, observe performance and refine the workflow.
This approach preserves the speed and creativity experimentation requires while maintaining a high bar for reliability. Guardrails like feature flags, staged rollouts and strong observability ensure learning and quality reinforce each other rather than compete.
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Cargill is a global company that provides food, ingredients, agricultural solutions and industrial products.
How do engineers influence innovation and product strategy?
We, as engineers, know the technical capabilities and limitations of the technology while keeping a pulse on emerging trends. At Cargill, we are encouraged to work closely with product, business and technology teams to see what’s possible, where a different approach could add value and what it takes to deliver and support. We work with the team to shape and evaluate potential solutions and think through what each option means for time, cost, quality, security and scalability. We can use product data as evidence to understand trends and friction, then work with product and business partners to weigh options for quality, security, cost and long-term flexibility. That gives the team a clearer basis for deciding what to build, improve, or explore. One thing I appreciate about Cargill is that our engineering teams have the flexibility to innovate in ways that make sense for our respective business functions while still benefiting from shared technologies and collaborative support.
How do you decide when to adopt new tech or tools?
Our process starts by evaluating what we already have: who is using it, how they are using it and where the gaps are. We focus on the problem we are trying to solve and compare the new technology with how we handle the problem today, including the cost, potential time savings, security and maintenance involved. We also consider whether we could build or extend a solution ourselves, how well the new solution fits with our existing capabilities and if there is a support model we can rely on. Before moving forward, we want to make sure it solves the right problem, fits our environment and is something teams can realistically use. There isn’t always a prescribed solution. Teams have the flexibility to determine what works best for their users while still benefiting from shared expertise, leadership support and guidance when needed.
What practices help engineers balance experimentation and reliability?
We balance experimentation with reliability by creating an environment where teams can explore new ideas while still protecting the systems people rely on. Strong testing, monitoring and release practices give us the confidence to try different approaches, learn from feedback and improve solutions over time.
In a recent project, I helped improve how we track application cloud migrations by automating data from an existing spreadsheet into a centralized application view. I explored different approaches, partnered with the UX team and worked with stakeholders to understand what would be most useful to them. Throughout the process, I had support from leaders who gave me the flexibility to build and refine the solution, helping ensure we delivered something that would truly work for our users. That’s the kind of engineering culture that helps us create a balance between innovation and reliability.
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NBCUniversal is a media and entertainment company.
How do engineers influence innovation and product strategy?
Engineers become influencers by leveraging practical knowledge and experience to both define the “art of the possible” as well as the “reality of the probable.” This means that a strategy needs to take into account not only the evolution of a product through advancing technology, but also ensuring that it stays maintainable and scalable. Many engineers have the dual experience of building new technology and supporting it. That perspective is very important when planning for the future. Often a new technology or tool is proposed that is very compelling from a feature perspective, but might lock the business into an expensive or suboptimal long term support stance. Engineers can evaluate a product with both lenses to ensure that innovation does not unknowingly drive operational expense. A good engineer can ensure that a product does not just work right now, but remains a value-add to the business for years to come.
How do you decide when to adopt new tech or tools?
All technology has a progression from the experimental to the operational and adoption depends on both technical readiness and scalability. This decision point comes when an experimental tool or technology can be successfully rolled out to the intended user base. Advancements in AI technology are making it easier and easier to quickly create technical “experiments”, but operationalizing them can be a challenge if you do not consider the larger goal. This does not just mean ensuring that the technology is ready, but the organization is as well. Determining readiness can be different for each technology. A small tool designed for a highly technical audience can be quicker and easier to adopt than deploying a new tool at the enterprise level. The decision has to take into account who the ultimate user base will be, not just whether or not the tool works.
What practices help engineers balance experimentation and reliability?
Standardized process, governance and observed data. Engineers should be free to experiment in sandbox environments to try out new ideas and test new technology. These should be continual seeding grounds for future products and enhancements. However, before they are rolled out at scale there needs to be a standardized process and governance around how technology is implemented once the value is identified. These don’t have to be heavy procedures or approval processes, but simply guardrails that new tools and technology need to meet in order to be rolled out. Things like user documentation, observability data and API standards, etc. all need to be considered. Many of these guardrails could (and should) be automated once standardized policies and measures are put in place to drive them.
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Expedia Group, Inc. is a global travel marketplace.
How do engineers influence innovation and product strategy?
The analytics platform team influences product strategy by looking across the organization and identifying where common problems are being solved repeatedly. Engineers often spot patterns that individual teams can’t see, then help turn those recurring challenges into shared capabilities that accelerate innovation across the business.
A good example is our investment in common metric definitions, semantic foundations and analytics ready datasets. Business meaning is often fragmented across queries, tools and the knowledge of a few experts. Engineers helped shape a strategy that makes trusted definitions reusable across analytics, experimentation and AI.
Engineers also explore emerging technologies, prototype capabilities and help decide which ideas should become part of the platform, including skills, plugins, model context protocol and AI-assisted analytics experiences. They shape capabilities that can be reused across teams and experiences. What I find most rewarding is solving a hard problem with product, analytics and business partners, then turning it into something others can build on.
How do you decide when to adopt new tech or tools?
Knowing when to adopt something new matters just as much as knowing when to retire something old. As the analytics platform team, we’re constantly evolving the platform while balancing innovation, operational excellence and long-term sustainability. We look beyond features and ask a few simple questions: Does it improve the experience? Does it make trusted data easier to access? Can it scale and be operated reliably? Does it simplify the platform rather than add complexity?
That thinking has guided some of our biggest decisions. We’ve moved our core analytics platform to the cloud, retired a legacy reporting platform and modernized analytics experiences while maintaining the reliability and continuity teams depend on every day. We also rigorously evaluated modern analytics engines against real workloads before selecting an approach that delivered the right balance of performance, flexibility, operational simplicity and scale.
The same principles continue to shape our investments today. We’re simplifying the analytics estate, strengthening our semantic foundations and creating supported paths for analytics and AI that are easier to adopt, operate and scale. The goal isn’t to adopt technology for its own sake. It’s to build a trusted foundation that helps teams move faster today while creating new possibilities for analytics and AI tomorrow.
What practices help engineers balance experimentation and reliability?
One of our core beliefs is that the safest path should also be the easiest path. We encourage engineers to experiment and challenge assumptions while creating an environment where promising ideas can evolve into reliable, reusable capabilities.
One of our model context protocol services is a good example. What started as an experimental, side-of-desk project quickly proved valuable. The team then focused on turning that prototype into a trusted, enterprise-ready capability that teams across the company could confidently build on.
That journey reflects our broader approach to innovation. We create space for experimentation, but we also invest in the engineering disciplines that allow successful ideas to scale safely. Strong observability, governance, automation and clear ownership help us move quickly without compromising reliability.
Just as importantly, we measure what we build. Adoption, performance, reliability, cost and user feedback are all signals that help us improve. The combination of experimentation and engineering discipline allows us to turn promising ideas into trusted capabilities at scale.
The Farmer’s Dog is a pet health and nutrition company that delivers healthy food options for pet owners.
How do engineers influence innovation and product strategy?
I love this question because I think it’s something that really sets us apart as we encourage engineers to be active contributors to innovation and product strategy, not just the people responsible for bringing ideas to life.
We bring engineers into conversations early, encourage them to challenge assumptions, contribute ideas and help shape the direction we’re headed. Some of the best opportunities come from engineers who deeply understand our systems, recognize what’s possible and aren’t afraid to ask, “what if we approached this differently?”
For me, it’s about creating an environment where engineers feel empowered to think beyond the code, take ownership of outcomes and have a meaningful impact on the business.
How do you decide when to adopt new tech or tools?
There’s no one-size-fits-all answer here. For me, it starts with understanding the problem we’re trying to solve and why. What are we hoping to accomplish? What does success look like? And how will this decision support us as we continue to grow?
I’m a big believer in choosing the right tool for the job rather than adopting technology simply because it’s new or exciting. Sometimes that means building something ourselves, sometimes it’s leveraging an existing solution and sometimes it’s a combination of both.
We also have to think beyond the initial implementation. The long-term cost of ownership, flexibility and the opportunity to free up engineering capacity all play into the decision. Ultimately, I want our teams spending their time solving problems that create meaningful value for our customers and business.
What practices help engineers balance experimentation and reliability?
I actually see thoughtful experimentation as a way to increase reliability, not something that has to come at its expense.
The key is creating an environment where engineers can test ideas, learn quickly and make informed decisions without introducing unnecessary risk to our customers. Practices like shadow mode, gradual rollouts, feature flags and strong monitoring give us the confidence to experiment while maintaining control over the customer experience.
I want our engineers to feel empowered to take calculated risks, challenge how we’ve done things historically and continuously improve our systems. When you build the right guardrails, experimentation and reliability can reinforce each other rather than compete.
