Natera is seeking a Senior Full-Stack ML Engineer to transform early-stage AI/ML prototypes into scalable, production-ready applications for therapeutic and diagnostic use.
Requirements
- Python, Flask/FastAPI or Django (for APIs/services)
- JavaScript/TypeScript with React or similar (for front-end interfaces)
- Cloud platforms like AWS, GCP, or Azure (especially GPU usage)
- Production-scale, multi-GPU AI inference frameworks like vLLM and DeepSpeed.
- Docker, Kubernetes, and related container/orchestration technologies
- Experience with experiment tracking, model registry, and observability tools (e.g., MLflow, W&B) is highly desired.
- Familiarity with model-serving tools (e.g., Triton, TorchServe, TensorFlow Serving) is a plus.
Responsibilities
- Productionize ML prototypes: Translate alpha-stage tools (e.g. Jupyter notebooks, python packages) into scalable, secure, and maintainable production systems.
- Full-stack engineering: Build and integrate both back-end services and front-end interfaces that are performant and user-friendly.
- Cloud and GPU infrastructure: Design and deploy applications using GPU-accelerated cloud infrastructure (AWS); including multi-GPU inference using frameworks such as vLLM and DeepSpeed for serving large-scale foundation models.
- AI Model Lifecycle: Oversee the full model lifecycle with versioning (MLFlow), performance monitoring (W&B), and updating strategies (A/B testing).
- Autonomous development: Operate independently with minimal oversight, taking ownership from handoff to final deployment, and iteratively improving products based on internal and external feedback.
- DevOps & CI/CD: Set up and manage robust CI/CD pipelines, monitoring tools, and testing frameworks to ensure reliability and reproducibility.
- Security & Compliance: Ensure that tools are developed with appropriate authentication, data privacy, and (where needed) HIPAA or regulatory compliance considerations.
Other
- Work closely with ML scientists, product leads, and clinical stakeholders to align engineering implementation with scientific and clinical goals.
- Demonstrated ability to work autonomously and prioritize in a fast-paced, interdisciplinary environment.
- Product mindset with an eye for usability, performance, and security.
- Comfortable reading scientific code and engaging with ML researchers.
- Passion for health innovation, biotech, or clinical impact.
