Top Machine Learning Jobs in St. Louis, MO

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Build and manage technical relationships with LATAM enterprise customers, helping them adopt JetBrains products, overcome technical barriers, and achieve business outcomes. Serve as a trusted technical advisor, customer advocate, and liaison with Support and Product Engineering. Partner with Account Executives and Sales on account strategies and expansions, recommend product enhancements, present product ideas, and maintain expertise in software development technologies.

Leads the architecture, research, development, deployment, and lifecycle management of enterprise AI and machine learning solutions, including generative AI, LLMs, and agentic systems. Partners with data scientists, engineers, IT, business stakeholders, and life sciences experts to deliver reusable, compliant, production-grade solutions across pharmaceutical R&D and corporate functions. Guides AI product strategy, responsible AI, data governance, MLOps, stakeholder adoption, and technical enablement.

Lead end-to-end delivery of complex enterprise programs for utility customers, coordinating Customer Success, Product, and Engineering from kickoff through adoption. Manage governance, scope, milestones, risks, dependencies, executive communications, and stakeholder alignment. Drive customer change management and improve delivery standards across the US team. Identify opportunities to apply AI and automation to reporting, data validation, and project setup.

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Lead research and end-to-end development of machine learning solutions involving LLMs, generative models, summarization, LLM-based evaluation, and recommendation systems. Translate advances in generative AI into business applications, optimize generated content for member impact, partner with engineering and ML teams, define technical roadmaps, and communicate complex concepts to stakeholders.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Build and scale AI-native healthcare products through full-stack applications, backend services, data pipelines, and AI systems. Design, test, deploy, and operate production-ready software, run A/B tests, automate manual workflows, and collaborate with product, operations, and AI/ML teams. The role requires end-to-end ownership of scalable, secure, reliable systems and features, with a focus on performance, data integrity, clean code, and measurable business outcomes.

Conduct applied AI research focused on explainability, reinforcement learning, retrieval-augmented generation, and long-context retrieval. Design, train, and evaluate machine learning models using large-scale healthcare data and distributed infrastructure. Build research prototypes and experimentation frameworks, validate measurable impact, and collaborate with ML Engineering, MLOps, and Product teams to transition innovations into production.

Own the vision, roadmap, and delivery of AI-enabled finance technology products. Translate finance and accounting challenges into scalable digital solutions using data, cloud, analytics, automation, and AI. Partner with senior stakeholders to prioritize outcomes, lead cross-functional delivery, monitor product performance, and improve adoption, usability, anomaly detection, insight generation, and data-driven financial decision-making.

Leads customer-facing development of AI agent systems, multi-agent workflows, BI and ML pipelines, and enterprise data governance solutions. Partners with Fortune 500 stakeholders to translate business and data challenges into production-grade technical solutions, develops reusable delivery patterns and internal tooling, influences product roadmaps, conducts competitive analysis, and mentors engineers and technical teams.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Run Discovered Labs’ founder-led content operation across LinkedIn and YouTube, from topic selection and editorial development through publication and performance analysis. Responsibilities include shaping founder content, coordinating external partners, repurposing long-form material, improving AI-supported content systems, and using engagement and pipeline data to guide editorial decisions. The role requires strong editorial judgment, hands-on execution, social content expertise, and the ability to work autonomously in an AI-forward, remote environment.

Coordinate and govern release cycles for the HIGLAS Product Suite within an IT service management environment. Responsibilities include managing release phases and tasks, maintaining release calendars, conducting quality checks, tracking risks and dependencies, presenting status to review boards, coordinating outage notifications, completing post-release reviews, facilitating cross-functional meetings, and maintaining procedures, reports, dashboards, and KPIs. The role also supports process improvement and AI-enabled governance workflows.

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Own ElevenLabs’ North American enterprise financial services sales motion across banking, insurance, lending, payments, and wealth. Define the ideal customer profile, sales methodology, pipeline and forecasting processes, land major enterprise accounts, and expand deployments. Build and lead the sales team, guide regulated buyers through risk, compliance, model-risk, architecture, and security reviews, and manage executive negotiations. Partner with engineering, product, marketing, and customer success to develop repeatable conversational AI sales strategies.

Leads eDiscovery and data analysis workstreams for complex cyber incident response matters, overseeing data assessment, review, extraction, normalization, entity resolution, notification, reporting, budgets, vendors, quality, and delivery. Advises legal teams and clients on technical strategies, risks, workflows, and findings. Manages multidisciplinary teams, develops defensible methodologies, mentors staff, resolves escalations, and advances Cyber eDiscovery processes through analytics, automation, and emerging technologies.

Serve as strategic commercial and technology legal advisor, drafting and negotiating software, SaaS, licensing, data, customer, and vendor agreements. Advise on AI products, governance, responsible AI, privacy, intellectual property, regulatory compliance, product launches, and legal risk mitigation. Partner with Sales, Product, Engineering, and executive teams while supporting international and broader legal initiatives. Use legal technology and AI tools to improve legal service delivery.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

Designs, develops, evaluates, and deploys AI and data-driven solutions using machine learning, deep learning, NLP, LLMs, intelligent agents, prompt engineering, and RAG. Builds scalable data pipelines, experiments with models, deploys AI services through APIs and cloud infrastructure, and monitors system reliability and performance. Collaborates with engineering, product, and customer-facing teams to translate business needs into production-ready solutions while addressing risks and continuously improving AI capabilities.

Leads strategy, roadmaps, business cases, adoption, and value measurement for enterprise AI products, beginning with AI-driven field-service dispatch optimization. Partners with data science, engineering, operations, finance, marketing, IT, and vendors to deliver measurable business outcomes, manage pilots and rollouts, establish responsible AI practices, and build a scalable AI product function.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.