Principal AI Engineer

POSITION SUMMARY

Natera is seeking a Principal AI Engineer to set technical direction for, and hands-on build of, the Vector Store GenAI Platform that powers document intelligence and retrieval across our Real-World Data (RWD) organization. The platform ingests clinical documents from multiple sources, indexes them for high-quality search, and makes that content available to downstream AI workflows and business use cases across oncology and other clinical domains.

This is a principal-level individual contributor role with architecture-plus-build ownership of retrieval, document processing, and ingestion systems. You will define platform strategy and sequencing, ship production code in a cloud environment, raise the reliability and quality of search and retrieval, and prove new approaches through POCs and benchmarks. You will partner with clinical, engineering, and product leaders to turn ambiguous priorities into a clear technical roadmap, establish standards for how retrieval serves agents and re-extraction workflows, and deliver measurable improvements in platform quality and operability—with high autonomy and limited day-to-day direction.

The ideal candidate combines deep hands-on engineering with a track record of owning complex AI platforms end to end. You set direction, implement critical path yourself, and elevate the technical bar across the teams that consume and extend the platform.

RESPONSIBILITIES

Own platform architecture and hands-on delivery for the Vector Store GenAI Platform, including search and retrieval, document processing and chunking, ingestion pipelines, and APIs that serve downstream AI workflows.

Set technical direction for hybrid search and retrieval quality; evaluate embedding models and retrieval approaches through working implementations, POCs, and benchmarks, and drive adoption of what works in production.

Advance document parsing and chunking so clinical content is indexed in a way that supports accurate, structured, and faceted retrieval across common document types.

Define and build stable, reproducible retrieval for agent and re-extraction workloads, including durable content identifiers, flexible context assembly, versioned indices, and quality or confidence signals.

Strengthen document classification and ingestion visibility so source material is discoverable, operable, and measurable at scale across historical and ongoing data flows.

Continuously improve retrieval quality through better deduplication, clinical concept normalization, and exploration of promising retrieval methods where they add clear value.

Integrate LLM capabilities into existing production systems with attention to performance, scalability, cost, observability, and safe operation in a regulated environment.

Partner with clinical AI, engineering, product, and SME stakeholders to prioritize workstreams, define success metrics, and sequence roadmap investments without disrupting the live platform.

Provide principal-level technical leadership through architecture decisions, design reviews, clear documentation, mentoring influence, and pragmatic tradeoffs across accuracy, latency, cost, and delivery timelines.

QUALIFICATIONS

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field; an advanced degree or background in a clinical or life sciences discipline is a plus.

10+ years of software engineering experience, including substantial ownership of production systems at scale.

5+ years building and shipping AI/ML-powered applications in production, with deep hands-on experience in retrieval-augmented systems, vector search, and document-centric AI pipelines—including indexing, retrieval quality, evaluation, and integration with downstream LLM or agent systems.

Demonstrated track record as a principal or equivalent IC: setting technical direction for a platform or multi-team capability, making architecture decisions that stick, and personally delivering critical path production code.

Strong hands-on expertise in Python and modern software engineering practices, including testing, code review, workflow orchestration, and agile delivery in large existing codebases.

Experience designing and operating AI systems in cloud environments, with AWS strongly preferred; experience with CI/CD, observability, and production support is expected.

Proven ability to work independently as an architecture-plus-build IC—influencing without direct authority, aligning stakeholders, and delivering end-to-end from strategy through integration and rollout—with strong communication across technical and non-technical audiences.

Experience with clinical or structured healthcare documents and document-centric search or RAG systems is strongly preferred.

Experience in healthcare, life sciences, or other regulated industries is strongly preferred.