Natera is looking to build and deliver the next generation of Generative AI and ML platforms to impact patient outcomes and clinical innovation, requiring a hands-on technical leader to design, build, and scale these enterprise-grade systems.
Requirements
- Expertise in building production-grade ML/LLM systems on AWS tech stack (E.g. Python, LangChain/Llamaindex, CrewAI, Vector databases, Agent runtimes, TensorFlow/PyTorch, Spark, MLflow).
- Proven track record with GenAI/LLMs: fine-tuning, RAG, prompt orchestration, safety guardrails, monitoring, and cost optimization.
- Deep knowledge of AI/ML intensive systems, distributed architectures, and cloud-native development.
- Familiarity with AI governance frameworks, LLM safety, drift detection, bias detection, hallucination, explainability, and compliance (e.g., HIPAA, CLIA, FDA).
- Expertise in building production-grade ML/LLM systems on AWS tech stack
- Proven track record with GenAI/LLMs: fine-tuning, RAG, prompt orchestration, safety guardrails, monitoring, and cost optimization.
- Deep knowledge of AI/ML intensive systems, distributed architectures, and cloud-native development.
Responsibilities
- Define the technical vision and architecture for Natera’s ML and GenAI platforms, ensuring scalability, reliability, and compliance across diverse use cases
- Build, operate, and evolve core AI platform components for standardized data access, LLM model registries for versioning and lifecycle tracking, evaluation pipelines for model validation and monitoring, vector databases, RAG frameworks, and agent frameworks for GenAI applications, prompt orchestration and guardrails for safe and compliant LLM deployments
- Design, build, and operate end-to-end ML/DL/FM infrastructure (feature engineering, distributed training, evaluation, deployment, monitoring) that are modular, reproducible, and auditable.
- Design, build, and operate reusable GenAI services such as unstructured data extraction, classification, summarization, generation, retrieval from knowledge bases, prompt optimization etc.
- Implement production-grade Gen AI and ML services and API’s that power critical workflows, from genomics analytics to clinical trial optimization to patient-facing solutions.
- Lead the deployment and scaling of large models (custom trained LLMs, multimodal, deep learning) using modern MLOps practices (Kubernetes, MLflow, AWS-native services)
- Embed governance and monitoring guardrails into AI and ML pipelines, including bias testing, safety, security, hallucination, explainability, PHI/PII redaction, audit trails
Other
- Act as the principal technical authority in AI/ML engineering — set coding standards, review designs, and ensure best practices in reproducibility, monitoring, and observability.
- Mentor and guide other engineers and data scientists, providing thought leadership on system design, optimization, and responsible AI.
- Influence cross-functional roadmaps by partnering with Product, Data Governance, and Engineering leadership to align delivery with business needs.
- Strong grounding in compliance-first engineering in healthcare, biotech, or diagnostics preferred.
- Excellent ability to influence across teams, mentor engineers, and set technical standards.
