Senior AI/ML Engineer

Role Overview The Senior AI/ML Engineer is responsible for designing, building, and deploying Natera’s Generative AI and Machine Learning platforms. The role needs excellent hands-on engineering excellence to build robust, compliant, and efficient Generative AI and ML platform components. This role requires deep expertise in Generative AI and machine learning engineering at scale, with a passion for building robust, compliant, and high-performance systems that directly impact patient outcomes and clinical innovation. You will design, build, and scale enterprise-grade AI/ML systems that power internal workflows (RD, Lab Ops, Clinical Trials, Billing, Patient/Provider engagement) and external-facing AI/ML platforms. You will design and build cutting-edge AI solutions leveraging agentic architecture, retrieval-augmented generation (RAG), vector search, feature stores, LLMOps, experimentation, observability, and compliance-first AI pipelines. You will be responsible for development of a production-ready Generative AI and MLOps platform with reusable components used to deploy multiple AI solutions across Natera’s business units. You will also develop clear standards and best practices established for AI/ML development across the organization. Key Responsibilities Platform Development 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 Generative AI Enablement 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 Integrate tooling and APIs that allow agents to interact with internal systems, retrieve data securely, and take action under strict controls Collaborate with research teams to prototype and productionize multi-agent architectures for workflow automation, report generation, and data synthesis. Infrastructure Automation Implement cloud-native infrastructure for large-scale model training and serving using Kubernetes, MLflow, Terraform, and AWS-native services Automate data and model pipelines for RAG, LLM fine-tuning, and agent orchestration Integrate observability tools (Datadog or equivalent) for real-time performance, drift detection and safety monitoring of AI outputs Optimize compute and storage architecture to ensure cost-effective scaling of large models and multi-agent workloads Partner with security, data governance, SRE, and application teams to productize platform capabilities Safety, Security Compliance Integration Embed compliance-by-design (HIPAA/CLIA/CAP/FDA/GDPR): PHI/PII handling, encryption, access controls, audit trails Implement guardrails: input/output filters, prompt hardening, allow/deny policies for tool execution, policy-as-code in CI/CD Bias/explainability hooks and automated evaluations for RAG/LLM/agents; drift and regression detection Technical Leadership Mentorship Establish golden paths (templates, examples, docs) and lead platform architecture reviews, code reviews, and design discussions Partner with data scientists, AI researchers, and product engineers to deliver reliable and maintainable AI services Mentor junior engineers in platform development, dist ... (truncated, view full listing at source)