Natera is looking to solve the problem of designing, building, and deploying Generative AI and Machine Learning platforms to directly impact patient outcomes and clinical innovation.
Requirements
- Expertise in building production-grade ML/LLM systems on AWS tech stack (Python, TensorFlow/PyTorch, Spark, MLflow/Kubeflow, vector DBs)
- Proven track record with GenAI/LLMs: fine-tuning, RAG, prompt orchestration, agentic systems, safety guardrails, monitoring, and cost optimization
- Hands-on with RAG systems (embeddings, vector DBs, retrieval policies) and LLM runtime operations (caching, quotas, multi-model routing)
- Experience building agentic AI platforms (LangChain, LlamaIndex, CrewAI, Semantic Kernel, or custom)
- Deep knowledge of data-intensive systems, distributed architectures, and cloud-native development
- Strong grounding in compliance-first engineering in healthcare, biotech, or diagnostics preferred
- Track record building secure, compliant data/AI systems and automating policy checks
Responsibilities
- Design and implement foundational GenAI services: vector search, prompt tuning, agent orchestration, document extraction, context/memory services, model/endpoint registry, feature/embedding stores, guardrails, and evaluation pipelines
- Build the underlying infrastructure for autonomous and semi-autonomous AI agents including support for agent collaboration, reasoning, and memory persistence, enabling continuous context-aware execution
- Build standardized APIs/SDKs that make it easy for product teams to compose, deploy, and monitor Generative AI workloads.
- Ensure platform components meet enterprise-grade requirements for scalability, latency, multi-region resilience, and cost efficiency
- Stand up LLM runtimes with token/rate governance, caching, and safe tool-use
- Implement RAG at scale: ingestion pipelines, chunking/embedding policies, hybrid search, relevance/risk scoring, and feedback loops
- Build agent orchestration (single & multi-agent) with planning, tool routing, shared/persistent memory, and inter-agent communication
Other
- 8+ years in software/ML engineering, with 5+ years in ML engineering at scale
- Masters degree in Computer Science, AI/ML, engineering or related field (preferred)
- Experience in healthcare, pharma, diagnostics, or other regulated industries (preferred)
- Excellent ability to influence across teams, mentor engineers, and set technical standards
- Ability to work in a remote setting
