Top Machine Learning Jobs in St. Louis, MO

The Senior Product Architect will design security products, collaborating with product management and engineering teams, focusing on architecture and functionality for high-level product features.

The Vice President of Underwriting will lead the company's underwriting strategy, focusing on growth acceleration while managing credit, fraud, regulatory, and reputational risks. This role involves overseeing underwriting processes, implementing advanced analytics, optimizing risk frameworks, and ensuring regulatory compliance across different merchant segments.

Transfyr seeks adaptable builders for various roles across CS, biology, and business. Responsibilities include developing sensor stacks, data harmonization, computer vision, machine learning systems, lab operations, project management, and business development.

The Vice President of AI will define and lead the AI strategy at Tempo, integrating AI capabilities into products, building teams, and ensuring ethical use of AI while driving business outcomes and customer value.

The Product Counsel Senior Director provides legal counsel on product development and AI, manages IP portfolios, oversees compliance, and advises on technology transactions.

Lead a team of advanced product analysts, conduct data analysis and statistical modeling, design experiments, and develop reporting tools to drive product decisions and business performance.

Lead federal sales engineering efforts: deliver tailored demos, respond to federal RFPs/RFIs, navigate agency security reviews and ATO processes, produce solution designs aligned to federal frameworks (FedRAMP, NIST), and advise product/GT M strategy for federal use cases in fraud, identity, and AML.

Design, develop, and deploy end-to-end AI/ML applications (RAG, chatbots, agentic workflows). Build scalable data pipelines, apply graph analytics, entity resolution, and NLP for risk scoring, anomaly detection, and investigative prioritization. Develop models, REST APIs, and deploy/manage solutions in AWS to support federal mission use cases.

Provide commercial legal support across Sales, Product, Partnerships, Procurement, and Engineering: draft and negotiate NDAs, SaaS and vendor agreements, advise on product and privacy/regulatory risks (GDPR, CCPA/CPRA, GLBA), improve contract lifecycle management, and respond to day-to-day legal inquiries and ad hoc projects.

Build and re-architect Shipt's LLM-native search stack including retrieval and ranking systems. Design multi-task and multi-objective models, integrate personalization signals, develop hybrid dense/sparse retrieval, and apply generative retrieval and query expansion. Analyze search data, report findings to leadership, and partner with senior scientists and ML engineers to improve relevance, revenue trade-offs, and operational metrics.

Lead and grow the AI Forward Deployed Engineering team, oversee customer AI/ML transformations, and develop strategic partnerships while managing key accounts.

Develop and scale reinforcement-learning systems that close the loop between automated labs and reward models. Build synthetic data engines and inference infrastructure for large-scale training, maintain evaluations of learned policies using proprietary experimental datasets, and address real-world challenges like experimental noise and expensive queries.

As a Senior Machine Learning Engineer, you'll design and develop infrastructure and models for AI-powered applications while collaborating with teams to enhance product functionality.

As a Senior Staff Machine Learning Engineer, you will lead the development and deployment of machine learning models, ensuring they drive business growth and meet performance requirements. You will collaborate with teams, manage data pipelines, and implement state-of-the-art solutions while continuously improving model performance.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

Lead design, training, fine-tuning, evaluation, and production deployment of TTS/voice-generation models. Translate research into reliable, low-latency production systems, define ML best practices, and collaborate across research, engineering, and product to drive execution.

Design and develop AI capabilities and scalable microservices. Collaborate to integrate AI models and ensure performance in cloud environments.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

The Director of Data Engineering, ML & AI leads data strategy and AI capabilities, managing a team to enhance data infrastructure, machine learning systems, and sustainability analytics while driving company-wide AI initiatives.

Manage AI/ML projects from Proof of Concept to Minimum Viable Product using Agile methodologies while ensuring stakeholder communication and project alignment.

The R&D Engineer will build and refine software solutions for defense use cases, prototype systems, and convert stakeholder feedback into technical requirements. Collaboration with engineering teams and customer-oriented engagements are key responsibilities.

Build and deploy production ML models and end-to-end pipelines to detect suspicious behavior; design evaluation, monitoring, and drift detection; integrate intelligence into product workflows; improve data quality and accessibility; and contribute to operational rigor, runbooks, and safe rollout practices in a security-sensitive environment.

As a Software Engineer in ML Infrastructure, you will develop scalable data pipelines, enhance data discovery, and collaborate with teams on data organization and ML features.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.