Machine Learning Jobs in St. Louis, MO

Validate and monitor in-house and third-party NLP, generative, and agentic AI models; review data, sampling, and modeling frameworks; develop reusable validation code/libraries; coach junior staff; produce detailed analytical reports for governance and regulators; and communicate findings with stakeholders, auditors, and regulators.

Lead end-to-end delivery of AI and ML services within Managed Services. Manage project planning, budgeting, execution, and quality. Mentor and develop team members, cultivate client relationships, implement model monitoring and operationalization practices, and collaborate with architects and engineers to ensure compliant, reusable AI solutions.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Leads procurement and strategic sourcing across client accounts and properties. Develops sourcing roadmaps, executes eRFPs, manages supplier relationships, audits, performance, contracts, governance, reporting, and cost-saving initiatives. Analyzes market and account data, applies AI and machine learning analytics, supports change management and training, and leads cross-functional improvement projects. The role requires client-facing stakeholder engagement, contract knowledge, analytical ability, and leadership experience in procurement, ideally within facilities-related categories.

Lead global strategy, design, deployment, and sustainment of physical security technologies (access control, CCTV, intrusion, converged systems). Drive AI/ML video analytics, oversee system health, incident response, audits, budgets, vendor contracts, dashboards, and mentor distributed security specialists.

Open general application for Rapsodo, a sports analytics company using computer vision and machine learning. Seeking driven team players passionate about sports and technology to contribute to state-of-the-art analytics solutions and growth across product teams. Applicants encouraged to apply to join a fast-paced, innovative company partnered with major sports organizations.

Lead design and build of a cloud-based big data analytics platform: implement analytics infrastructure, integrate streaming and batch data, develop distributed processing pipelines, manage predictive analytics models, and apply machine learning and Bayesian methods. Work involves Scala on OS X and AWS, Java and Clojure, Kafka/Samza, NoSQL, and following TDD, Agile, CI/CD practices.

As an Exploitation Specialist/Imagery Scientist, you'll provide expertise in SAR imagery, assess sensor capabilities, and analyze geospatial data to enhance operational intelligence and machine learning applications.

The role involves analyzing Synthetic Aperture Radar imagery, curating data for Machine Learning, and recommending strategies based on sensor characteristics.

The Imagery Scientist will provide geospatial expertise and analysis to enhance data curation for Machine Learning testing, improve sensor platform integration, and automate scientific processing workflows.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Lead multiple engineering teams to design, deliver, and operate scalable Nursing and Medical Education products. Drive cloud modernization, API-first platforms, generative AI/ML adoption, architecture governance, and cross-functional alignment with product, data, and quality. Build and mentor engineering leaders, define multi-year roadmaps, and lead workforce and succession planning to ensure delivery and continuous improvement.

Lead multiple engineering teams delivering scalable Nursing and Medical Education products. Set technical strategy, roadmaps, cloud modernization, API-first architecture, AI-enabled capabilities, governance, and engineering standards. Partner with product, data, UX, and business stakeholders, mentor leaders, drive operational excellence, and manage workforce and succession planning.

Serve as a technical SME in AML, sanctions, and third-party risk to support sales and client engagements. Lead RFPs, design API-based integrations, build data pipelines, scope bespoke projects, advise on data modeling and analytics, and drive solution positioning and product feedback across the Americas.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Design, build, validate, and deploy predictive models using Python and SQL. Apply causal inference, A/B testing, matching models, and AutoML to evaluate interventions. Partner with product and engineering to deliver scalable analytics, ensure data quality and model performance, and communicate insights to stakeholders.

As Director of Product, you will define and drive Kobie's data platform strategy to enhance marketing and loyalty programs, focus on AI integration, and develop commercial models.

Design, train, validate, and deploy ML models and production inference pipelines (CV, NLP, geospatial). Build MLOps workflows, scalable APIs, and AWS-deployed AI workloads. Monitor model drift, optimize performance, collaborate with data scientists, architects, and stakeholders, and document processes and integrations.