Top Machine Learning Jobs in St. Louis, MO

As Co-Founder & CEO, you will define strategy, lead customer development, drive product iterations, raise capital, and build the founding team for InfluTracker, an AI content intelligence venture.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Lead design, build, and scale backend and frontend components for Pearl's clinical product suite. Own engineering architecture and deployments, integrate systems with ML/computer vision pipelines, use AI-assisted development tools to accelerate delivery, improve engineering velocity and quality, and mentor teammates in AI-augmented workflows.

Lead enterprise-wide continuous improvement strategy across multiple sites, driving Lean and Six Sigma initiatives, AI-enabled analytics, automation, and standardization. Oversee CI projects, KPI alignment, capability building, and cross-functional collaboration to improve quality, productivity, cost, and service.

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.

The Director of Artificial Intelligence will define, lead, and implement AI strategies, managing R&D, building teams, and ensuring ethical AI use to drive business outcomes.

Lead Data Science initiatives, build AI/ML solutions, and mentor teams in a collaborative environment, driving innovation in a leading construction equipment venture.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

The Principal AI/ML Researcher/Engineer will lead research and development of reasoning, planning, and decision-making systems, integrating multi-agent coordination with reinforcement learning to enhance Airbnb's AI capabilities for personalized experiences.

Lead design and delivery of a unified, real-time billing platform that embeds AI/ML to automate reconciliations, detect anomalies, enable predictive insights, and create autonomous finance workflows across global billing operations.

Own end-to-end lifecycle for AI features: define requirements, run model evaluation and experimentation, maintain capability-level AI roadmap, apply responsible AI principles, measure success metrics, iterate with engineering, UX, security, and go-to-market to deliver customer-ready AI capabilities.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Lead product marketing for ExtraHop's Agentic SOC, shaping category narrative, translating customer and partner insights into launches, sales enablement, content, and go-to-market programs while engaging security practitioners, product, and partners to drive adoption of AI-powered security operations.

Architect and deliver AI-driven documentation systems (TMS/CMS/automation) for regulated technical publications. Design and deploy ML/NLP/Generative AI tools, optimize translation memory, support DITA/XML workflows and FrameMaker templates, build Python scripts for publishing and content reuse, and evaluate documentation workflows to improve localization, quality, and analytics.

Lead and scale engineering teams building Protect, a real-time multi-signal fraud detection platform. Set technical strategy, develop managers and senior engineers, deliver distributed systems and APIs, improve architecture, observability, reliability, and developer productivity while partnering with Product, Research, Security, and exec stakeholders.

Design, build, and deploy AI-driven documentation and localization systems (TMS/CMS), improve translation memory and automation, support DITA/XML workflows, develop Python scripts for publishing, and implement generative AI/NLP solutions within a regulated life-sciences environment.

Lead client-facing Data & Analytics engagements across pre-sales, architecture, and delivery. Define strategy, roadmaps, solution designs, and SOWs for modern data platforms, pipelines, governance, analytics, and AI enablement. Drive practice development, create reusable IP, mentor teams, and ensure quality delivery and strong business outcomes.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

The Staff Product Manager will define product strategy, lead the product lifecycle, collaborate across teams, and drive innovation in claims processing software.

The Director of System Safety oversees safety functions for autonomous vehicles, managing safety cases, deployment plans, and regulatory relationships while leading a multi-disciplinary safety team.

The Global Process Owner for S&OP leads the design and continuous improvement of the S&OP process, ensures collaboration across functions, and drives digital transformation and automation within the organization.

The VP of Data and AI will define and execute data and AI strategies, lead technical teams, oversee data platform infrastructure, and drive measurable business outcomes.