Top Remote Machine Learning Jobs in St. Louis, MO

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

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, build, and operate production-grade ML systems for personalization, search, and recommendations. Own end-to-end pipelines from data ingestion and modeling to deployment, monitoring, and iteration, ensuring scalable, reliable, and observable services.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Responsible for designing, building, and operating AI-powered features, scalable APIs, and maintaining integrations within ClickHouse's AI/ML ecosystem.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

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.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Looking for a Machine Learning Engineer to enhance AI capabilities in image analysis and structured data. Responsibilities include developing computer vision models, deploying ML systems, and collaborating with engineering teams.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Design and maintain datasets, build and evaluate domain-specific ML and vision-language models for real-time privileged access threat detection; deploy and optimize Python/Docker inference services integrated with WebSocket/WebRTC and protocol-level interfaces; monitor and document production models.

Design and implement AI-powered data pipelines and LLM-based mapping to parse, normalize, and validate structured and unstructured data. Build mapping logic, ingestion templates, and load-ready outputs; integrate ML/AI solutions with enterprise systems (APIs, databases, Azure); validate accuracy with stakeholders; document for scalability and production readiness while reducing manual mapping and improving data quality.

Seeking a Principal AI/ML Researcher and Engineer to lead research and development in Bayesian Learning and Reinforcement Learning. This role involves architecting large-scale AI systems, model development, and driving technical strategy for innovative AI platforms that enhance personalization and decision-making at Airbnb.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

Design, build, and own production ML and LLM-driven features (RAG, prompt engineering, fine-tuning, semantic search). Write maintainable code, create design docs, troubleshoot issues, collaborate with product/design, and participate in Agile development. Work with Python/Java/JavaScript, ML frameworks, and cloud/container technologies.

Drive technical direction and architecture for AI Context systems across HubSpot's CRM: develop, evaluate, productionize, and monitor ML/LLM models; build scalable data processing, retrieval, feature generation, and serving systems; lead cross-team initiatives, mentor engineers, and measure customer and business impact while addressing privacy, bias, security, reliability, cost, and data governance.

Design, build, and operate cloud-native data and ML infrastructure for real-time and batch workloads. Implement reproducible training, evaluation, and inference pipelines; integrate event streams; monitor data quality, model performance, latency, and cost; automate deployments with IaC and CI/CD; collaborate with product, data science, and security teams; produce docs, dashboards, and runbooks.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.