Top Machine Learning Jobs in St. Louis, MO

The AI Developer is responsible for designing and deploying AI solutions, including autonomous agents and predictive models, and integrating them into business operations.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

The Machine Learning Engineer will develop editable visuals combining computer vision and machine learning, enabling chat-driven figure editing and leveraging biological understanding to produce accurate visuals.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

AVIA seeks experts in Digital Health and AI for advisory roles to support healthcare systems' technological transformations as part-time, project-based consultants.

Lead and scale full-stack engineering teams for Yahoo Mail; define engineering vision and roadmap; drive AI-native development and AI transformation using Generative AI/LLMs and agents; partner with Product, Design, and Data Science to deliver scalable cloud-native mobile and web experiences; hire, mentor, and retain engineering talent while guiding architecture, technical reviews, and platform modernization.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

Build full-stack marketplace features and ML Ops infrastructure for search, ranking, and renter-facing products. Develop model deployment pipelines, feature stores, real-time data pipelines, and monitoring using Chalk and Vertex AI. Collaborate with data scientists to productionize models, improve system performance and reliability, write tested code, participate in reviews, plan technical work, and communicate effectively across distributed teams.

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Design, build, and productionize ML and AI systems (LLMs, embeddings, recommendations, forecasting). Develop end-to-end ML workflows, scalable data pipelines, semantic models, and CI/CD for ML. Collaborate with product and engineering, mentor engineers, and influence platform architecture and standards.

Lead development and productionization of large-scale ranking and personalization ML systems. Build data pipelines, features, and models (batch and real-time), collaborate cross-functionally, run experiments, and improve infrastructure for search, recommendation, and marketplace optimization.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

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.

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

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design, implement, and scale web and data pipeline solutions focused on generative AI and LLMs. Improve AI/ML infrastructure for model development, training, deployment, and prompt engineering. Partner with product and stakeholders to prioritize applied AI use cases, ensure PHI security and regulatory compliance, mentor junior engineers, and contribute APIs and integrations to embed generative AI into healthcare applications.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.