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About This Role
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.
- Experience designing retrieval interfaces used by agents or multi-step LLM workflows is a plus.
OUR OPPORTUNITY
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.
WHAT WE OFFER
Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!
For more information, visit www.natera.com.
Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.
All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws.
If you are based in California, we encourage you to read this important information for California residents.
Link: https://www.natera.com/notice-of-data-collection-california-residents/
Please be advised that Natera will reach out to candidates with a @natera.com email domain ONLY. Email communications from all other domain names are not from Natera or its employees and are fraudulent. Natera does not request interviews via text messages and does not ask for personal information until a candidate has engaged with the company and has spoken to a recruiter and the hiring team. Natera takes cyber crimes seriously, and will collaborate with law enforcement authorities to prosecute any related cyber crimes.
For more information:
- BBB announcement on job scams
- FBI Cyber Crime resource page
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Natera, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Natera AI Hiring
Natera has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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