Top Artificial Intelligence Jobs

Senior executive leading end-to-end global production and deployment for AI hardware across China, the US, and Mexico. Own manufacturing operations, supplier and logistics relationships, deployment playbooks, cross-functional execution, cost and budget collaboration, and build a scalable operations organization focused on quality, lead times, and continuous improvement.

Develop and test AI software using large datasets, Python, JavaScript, and C++. Evaluate algorithms, statistics, and data to improve industrial applications. Collaborate with machine learning and data engineers on end-to-end AI and integration solutions, stay current with AI trends and regulations, and present project progress to leadership and stakeholders.

Designs and executes strategic, operational, and tactical wargames supporting U.S. Army training, experimentation, capability development, and analysis. Develops game mechanics, adjudication systems, decision models, and adversary behaviors grounded in military doctrine and threat capabilities. Applies game theory, operational analysis, and data-driven evaluation to produce recommendations for senior leaders. Leads workshops and tabletop exercises while collaborating with developers, data scientists, and AI teams to integrate wargames into simulation environments.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

Lead exploratory AI research and innovation initiatives, evaluating emerging techniques such as neuro-symbolic AI, causal AI, agentic AI, reasoning systems, and knowledge-enhanced machine learning. Design prototypes, assess capabilities and risks, identify business applications, and collaborate with engineering teams to transition successful concepts into production. Represent TransUnion through publications, patents, and presentations while shaping long-term AI strategy.

Own the end-to-end product direction for Brex’s Finance Admin AI agents, including strategy, roadmap, agent behavior, prompting, evaluation, and quality. Partner with engineering, design, research, and finance administrators to build trustworthy agents for expense auditing and business analysis. Translate complex finance workflows into production capabilities, prototype and analyze agent performance, establish feedback loops, and determine when agents should recommend or act autonomously.

Own the research-engineering loop for turning base models into capable, reliable agents. Design RL environments, training and evaluation data, post-training experiments, reward systems, and regression suites. Analyze model behavior, diagnose reward hacking and other failures, improve distributed training and rollout infrastructure, and translate successful research into reproducible production pipelines. The role requires strong Python and software-engineering skills, modern language-model experience, deep-learning knowledge, and reinforcement-learning intuition.

Lead advisory engagements with financial institutions from pre-sale through expansion. Understand bankers’ and investors’ workflow challenges, demonstrate tailored AI solutions, build executive relationships, and establish credibility across financial services use cases including research, M&A, and diligence. Partner with marketing on industry content, serve as the voice of the customer for sales and product teams, tailor new solution introductions, and conduct customer and competitor analysis.

Create and execute AI-focused content and social media strategies across X, LinkedIn, and YouTube. Responsibilities include writing threads, articles, and founder-led content; scheduling posts; monitoring trends; engaging communities and key opinion leaders; tracking performance metrics; and collaborating with engineering and business development teams. The role requires strong AI/LLM knowledge, excellent copywriting, social media fluency, and the ability to use AI tools and workflows.

Leads a team of 6–8 engineers responsible for financial ledgers, reporting, analytics, and correctness across a revenue cycle platform. Owns roadmap, hiring, career development, customer escalations, financial data migrations, OLAP modernization, parity checks, reconciliation, monitoring, and cross-team delivery. Partners with CFOs, comptrollers, product, payments, data infrastructure, and operations to ensure accurate reporting and reliable financial systems.

Leads enterprise AI data risk management and governance across data quality, lineage, ownership, controls, transparency, and regulatory requirements. Partners with business, technology, risk, compliance, legal, privacy, model risk, and audit teams to assess AI data risks, close control gaps, establish governance standards, and support responsible AI adoption. Advises senior leaders, facilitates cross-functional decisions, and mentors analysts supporting AI governance and data management.

Own the strategy, roadmap, quality, compliance, and deployment speed of Salient’s omnichannel AI agent product across voice, text, email, and chat. Define evaluation metrics, servicing workflows, guardrails, and instrumentation while working closely with engineers, lenders, and servicing teams. The role requires shipping LLM-powered products, analyzing real conversations, improving model and product performance, and accelerating lender implementations. It is an in-person San Francisco role with substantial ownership and potential to build a product team.

The Data Scientist develops advanced analytics, AI, machine learning, predictive models, and business intelligence solutions supporting pharmaceutical commercial strategies. Responsibilities include analyzing complex healthcare and longitudinal patient data, building scalable algorithms and automated analytics capabilities, integrating and governing cloud data, and communicating insights to technical and non-technical stakeholders. The role partners cross-functionally to support market intelligence, customer segmentation, targeting, promotional measurement, and data-driven decision-making across oncology, rare disease, and CNS franchises.

Manage enterprise customer accounts throughout the sales lifecycle, from pipeline generation through deployment, expansion, and renewal. Develop account strategies, own revenue targets, forecast revenue, and analyze account metrics. Partner with engineering, solutions, research, marketing, and product teams on complex customer programs. Engage C-level executives, communicate technical AI concepts, monitor market intelligence, support recruiting, and help build scalable company processes and culture.

Leads early supplier involvement for AI data center hardware, driving hardware co-design, DFM/DFT, liquid cooling, high-speed PCBAs, power delivery, rack integration, prototyping, cost modeling, production yield, and supply-chain risk mitigation. Partners with engineering teams and strategic suppliers to accelerate EVT/DVT, qualify alternate sources, and ensure scalable, high-quality manufacturing of high-density compute platforms.

Leads the design, development, and deployment of AI-powered healthcare solutions using Java, Spring Boot microservices, HL7 FHIR, Da Vinci guides, Kafka, relational databases, Docker, Kubernetes, and cloud platforms. Builds REST APIs and event-driven systems across claims, eligibility, prior authorization, and provider domains. Establishes CI/CD automation, observability, security, and responsible AI practices while evaluating emerging technologies and improving operational workflows.

Build and optimize foundational AI infrastructure for large-scale training, reinforcement learning, rollout, and inference workloads. Responsibilities include profiling bottlenecks, optimizing GPU and distributed execution, developing scheduling and load-balancing systems, modeling performance and capacity, improving fault tolerance, and creating benchmarks and observability tools. The role partners closely with AI researchers and systems engineers to improve throughput, reliability, utilization, latency, and cost across complex GPU clusters.

Leads the planning, design, specification, deployment, and scaling of advanced electrical, instrumentation, and automated test systems across manufacturing and laboratory environments. Coordinates equipment teams and test platform developers, evaluates sensors and technologies, supports NPI and DFMA/DFT initiatives, and converts test data into operational insights. Provides technical leadership, mentoring, troubleshooting, procurement support, and cross-functional program ownership while applying networking, software, data analysis, lean manufacturing, and Industry 4.0 technologies.

Leads AI transformation strategy and delivery for energy, transportation, and industrial clients. Identifies high-value AI use cases, develops roadmaps, manages cross-functional implementation programs, evaluates AI platforms, and drives process redesign, governance, adoption, training, and change management. Partners with executives, operational leaders, technical teams, and Applied AI Leads while overseeing multiple workstreams, measuring business impact, presenting recommendations, mentoring consultants, and developing reusable transformation frameworks.

Lead AI transformation strategy and delivery for energy, transportation, and industrial clients. Identify high-value AI use cases, develop transformation roadmaps, manage cross-functional implementation, establish governance and KPIs, guide AI platform adoption, lead process redesign, drive organizational change, and advise executive stakeholders. The role emphasizes strategy, stakeholder alignment, consulting delivery, and adoption rather than coding or model development.

Develop evolutionary algorithms for autonomous scientific discovery, enabling populations of models, hypotheses, programs, and representations to evolve through iterative search, selection, evaluation, and recombination. Build Bayesian and probabilistic methods for candidate ranking, uncertainty estimation, diversity preservation, and quality measurement. Establish benchmarks, integrate algorithms into autonomous-science pipelines, publish research, guide technical direction, and mentor colleagues as a senior individual contributor.

Develop AI-powered software applications, services, and integrations for enterprise-scale industrial automation. Work with software engineers and architects across the full development lifecycle, including agile development, system design, test-driven development, test automation, and build configuration management. The role includes mentorship, collaboration with development teams, and hands-on exposure to modern AI and software engineering practices in a hybrid Milwaukee or Mayfield Heights setting.

Analyze complex operational and intelligence datasets to identify trends, measure effects, and support national security decision-making. Build automated workflows, data pipelines, dashboards, AI/ML and NLP solutions using Python, R, and enterprise data platforms. Integrate structured, unstructured, geospatial, social media, and operational data into actionable insights while collaborating with intelligence, operations, research, and technology teams. The role requires an active TS/SCI clearance and supports OIE and irregular warfare missions.

The Senior Software Engineer will build AI solutions for clinical workflows, collaborating with teams to enhance software architecture and mentor other engineers.

Lead the architecture, development, deployment, and reliability of production AI systems. Establish technical standards, make architecture decisions, build observability and evaluation frameworks, mentor engineers, and translate client and product needs into scalable AI solutions. The role requires strong software engineering, full-stack or backend development, system design, technical leadership, and expertise with modern AI tooling.