Machine Learning Engineer

ML Engineer — ML Pipeline Engineering, Training Infrastructure, Model DeploymentLocation : NYC / SF / Austin metroThis is an ML infrastructure and pipeline engineering role. The ML Engineer builds the training and inference infrastructure that the Applied AI Engineers use to build production applications. This person makes models production-ready — with clear handoff contracts, performance gates, and packaging standards.The primary focus is ML pipeline engineering: building training workflows, experiment tracking, benchmarking infrastructure, and model deployment pipelines. GPU and inference optimization is secondary ML experience in any production deployment, infrastructure, or pipeline engineering. This role builds the infrastructure, not just the models.Pure data scientists or ML researchers without software engineering depth will not be considered, the primary focus is ML pipelines, not GPU optimizationTECHNICAL SKILLSPrimary language: PythonPyTorch — confirmed as the ML framework.ML pipeline infrastructure: training pipelines, inference services, experiment tracking, benchmarking, model packaging and deployment.Ranking and triage models for AI agent / human operator routing.Retrieval, ranking, categorization, and generative AI over large-scale unstructured healthcare data.Specific MLOps tools (MLflow, Weights & Biases, etc.)Required Experience• ML pipeline engineering experience — building training workflows, experiment tracking, model deployment infrastructure.• Production ML system experience — deploying models into production environments with performance gates, monitoring, and maintenance. Not just training models.• PyTorch proficiency.• ML fundamentals..• Software engineering foundation — 5 years minimum with 2 years in ML.• Ability to prototype and scale: 0→1 development of new ML capabilities, then hardening for production use.Preferred ExperienceHealthcare data experience — clinical documentation, medical coding, claims, revenue cycle workflows. Highly preferred; candidates with RCM background are prioritized.• Experience with retrieval systems, search, ranking, embeddings, or long-context information retrieval.• Experience designing systems that route work between AI and human operators (human-in-the-loop routing, confidence thresholds, triage models).• Experience with model fine-tuning, distillation, or reward modeling.• Experience building evaluation frameworks, LLM judges, or expert-labeled benchmarks.• Startup or research-to-production environment experience