Senior Generative AI Engineer

The Senior Generative AI Engineer is responsible for designing, building, and deploying Natera’s Generative AI and Machine Learning solutions. The role needs excellent hands-on AI engineering excellence to build robust, accurate, and reliable AI solutions. This role requires deep expertise in Generative AI and machine learning engineering at scale, with a passion for building solutions that directly impact patient outcomes and clinical innovation.

In this role, you will develop enterprise-grade AI/ML solutions that power internal workflows (R&D, Lab Ops, Clinical Trials, Billing, Patient/Provider engagement) to automate business processes.

You will  also build cutting-edge AI solutions leveraging agentic architecture, retrieval-augmented generation (RAG), vector search, feature stores, LLMOps, experimentation, observability, and compliance-first AI pipelines.

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  Design, build, and operate LLM-powered systems used in production, from initial design through deployment and iterations

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  Build scalable agentic AI automation solutions, selecting appropriate patterns (reasoning, memory, agent frameworks, MCP’s, workflow orchestration, fine-tuning) based on business requirements

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  Implement GenAI patterns such as RAG, tool/function calling, and multi-step workflows, selecting approaches based on accuracy, reliability and cost

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  Develop and maintain data ingestion and retrieval pipelines, especially for unstructured or semi-structured documents

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  Fine-tune and adapt open-source or commercial LLMs for domain-specific tasks when appropriate

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  Set quality, evaluation, and reliability standards for GenAI systems, including testing, monitoring, observability, and failure handling

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  Make system-level tradeoffs across model choice, latency, cost, accuracy, and operational complexity, and guide teams through those decisions

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  Deploy and monitor GenAI services on AWS, optimizing for latency, cost, and system stability

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  Collaborate with product managers and domain experts to translate requirements into technical solutions

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  Establish golden paths (templates, examples, docs) and contribute to shared GenAI libraries, patterns, and best practices used by other engineers

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  Provide technical guidance and mentorship to mid-level engineers

• 8+ years of experience in software engineering, ML engineering, or applied AI

• Proven experience building and operating LLM-based systems in production

• Strong Python skills and experience with PyTorch and/or Hugging Face

• Experience building agentic AI solutions using agent frameworks (LangChain, CrewAI etc.) and agent execution engines (AWS Bedrock etc.)

• Solid understanding of RAG architectures, embeddings, vector databases, and prompt orchestration

• Experience deploying AI systems on AWS (e.g., SageMaker, Bedrock, EKS/ECS, Lambda, S3)

• Strong debugging skills and comfort working across model, data, and infrastructure layers

• Ability to work independently on complex problems and communicate clearly with cross-functional partners

• Masters degree or PhD in Computer Science, AI/ML, engineering or related field

• Experience in healthcare, pharma, diagnostics, or other regulated industries

• Familiarity with AI governance frameworks, bias detection, explainability, and compliance (e.g., HIPAA, CLIA, FDA)

Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.

The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.