Top Entry Level Machine Learning Jobs in St. Louis, MO

This is an open talent‑community posting inviting U.S.-based candidates across multiple disciplines (software, AI/ML, XR, UX/UI, DevOps, cybersecurity, data, instructional design, etc.) to submit a resume and brief introduction. HTX Labs will retain candidate information for future contract-to-hire opportunities. Must be authorized to work in the U.S.; no visa sponsorship available. Remote-first role supporting immersive learning for Department of Defense clients.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

The Data & Analytics team at Surescripts transforms healthcare data into actionable insights, collaborating with various stakeholders to support critical decisions across the healthcare ecosystem.

The Enrollment Advisor helps prospective patients through the onboarding process, providing information and support throughout their enrollment in Revero's program.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Lead onboarding and training for new customers of an AI-powered construction SaaS. Customize training, run webinars and office hours, create documentation, drive user adoption, surface issues with support, and collaborate with product and customer success to refine onboarding processes.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Build and deploy AI-powered applications for customers, combining Python and TypeScript application engineering with LLMs, generative AI, agent orchestration, APIs, distributed systems, and cloud platforms. Partner with stakeholders to understand requirements, design scalable architectures, rapidly prototype solutions, integrate AI capabilities, and evolve proofs of concept into production systems. The role also involves customer-facing consulting, technical decision-making, and collaboration throughout the solution lifecycle.

This is a general talent-community application inviting candidates across many functions to submit resumes. Recruiting will review and retain applications for future openings; candidates should highlight experience, accomplishments, and interests for potential remote roles.

Design and build a hybrid rule-based and machine-learning system that maps contractor free text to a four-tier SAP catalog hierarchy, detects anomalies, learns from operator corrections, and exposes the model through a microservice. Responsibilities include architecture documentation, accuracy-threshold recommendations, classification pipeline development, model evaluation, feedback-loop design, and collaboration during Agile sprint reviews.

Build and deploy deep-learning models for financial market data, owning the full workflow from raw data and experiments through daily production inference. Responsibilities include managing small-scale GPU infrastructure, conducting leakage-proof and regime-aware evaluations, reproducing research, reading recent AI literature, and communicating findings. The role requires strong ML fundamentals, Python and PyTorch expertise, hands-on infrastructure skills, rigorous experimentation, and self-directed remote work. Trading experience is not required.

Paid 3-month remote contract for students or recent grads to build and test LLM-based solutions using the Token Factory inference platform. Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization. Run performance and quality experiments and contribute to internal tooling and automation. Mentorship provided; strong performers may be considered for full-time roles.

Design and build AI assistants for interactive 3D workflows: build NLP pipelines, integrate cloud and client LLMs, design knowledge graphs, fine-tune foundation models, prototype AI features, and improve personalization and system performance.

Build and maintain Quora's ML platform and ranking infrastructure to serve large-scale ML models. Work on GPU model serving, distributed systems, developer tooling, feature store modernization, and on-call rotation to ensure high availability, performance, and cost efficiency.