Top Machine Learning Jobs in St. Louis, MO

Lead enterprise data security architecture to protect sensitive data across its lifecycle. Define strategies for encryption, key management, tokenization, masking, DLP, classification, database security, and Zero Trust. Advise execs and engineering on secure design patterns for data platforms (e.g., Starburst), cloud/on-prem systems, APIs, data pipelines, and AI/ML workloads. Develop roadmaps, evaluate technologies, mentor staff, and drive secure-by-design adoption across the organization.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Lead the strategy, roadmap, launch, adoption, and ongoing improvement of an AI-powered clinical data insights platform. Partner with Data Science, Engineering, and application teams to create reusable capabilities, define platform metrics, prioritize investments, evaluate build-versus-buy decisions, and align senior stakeholders across clinical, technical, and business objectives.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Leads complex enterprise technology initiatives, including ERP implementations, BI, data analytics, AI, and systems integration projects. Manages planning, scope, timelines, resources, budgets, risks, dependencies, stakeholder communications, executive reporting, and cross-functional delivery across Product, Engineering, Data, and business teams. Drives process improvements and ensures projects meet business objectives, customer outcomes, and delivery commitments.

Own end-to-end SaaS implementations from solution alignment through configuration, data preparation, training, UAT, go-live, and handoff. Serve as the primary customer and project lead, managing timelines, risks, dependencies, stakeholders, and readiness. Configure workflows and platform settings, troubleshoot data and configuration issues, coordinate cross-functional teams, and identify implementation improvements. The role requires strong customer communication, project management, SaaS configuration, data handling, and business workflow analysis skills.

Own the technical sales cycle for Databricks consulting engagements, including discovery, architecture design, solution scoping, estimation, proposals, demos, proofs of concept, workshops, and statements of work. Design lakehouse, data engineering, analytics, governance, security, and AI architectures. Partner with sales, Databricks, alliance, and delivery teams while ensuring effective handoffs into implementation.

Own complex post-sale technical relationships from onboarding through adoption and renewal. Lead implementations, integrations, configurations, and security reviews; advise strategic customers on identity, access, device trust, and SaaS security. Partner with Customer Success and cross-functional teams to drive customer value, manage risks, support expansion, create scalable resources, and mentor team members while serving as an escalation point.

The Senior Display Media Account Manager develops and executes digital media strategies, focusing primarily on Amazon DSP, while fostering client relationships, providing analytics, and driving business growth for clients.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Supports payer modernization and health services consulting engagements by analyzing operational challenges, optimizing processes, applying business analytics, engaging stakeholders, implementing quality standards, and guiding transformation initiatives. Develops operational support frameworks, conducts market research, manages change, and advises clients on improving service delivery and business performance while mentoring junior team members.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Build and deploy mechanistic, hybrid, and data-driven models for pharmaceutical CMC processes, including drug substance, drug product, packaging, labeling, technology transfer, and manufacturing. Integrate process and experimental data into FAIR data products, analytics, dashboards, and AI-enabled tools. Partner with technical teams and CDMOs to support process understanding, forecasting, risk assessment, regulatory filings, and technology-transfer decisions while owning the digital roadmap and mentoring colleagues.

Design and develop scalable backend systems using Kotlin, Micronaut, and Spring. Build RESTful APIs, optimize PostgreSQL and Elasticsearch databases, implement RabbitMQ-based asynchronous processing, integrate AI tools, and create automated tests. Deploy and monitor services on GCP using Docker, Kubernetes, and Terraform. Collaborate cross-functionally, participate in code reviews, troubleshoot performance issues, and deliver reliable backend products.

Senior Product Manager responsible for FinCrime product strategy and delivery across fraud prevention, scams, payment risk, vendor integrations, and cryptocurrency-related products. Owns the full product lifecycle, global feature rollouts, stakeholder management, metrics, regulatory alignment, and customer experience. The role requires hands-on experience with fraud analytics, risk and financial modeling, machine learning, AI tools, APIs, payment systems, and financial-services compliance.

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.