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About This Role
Clinical Review RN, Oncology AI Review
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### Position Summary
Natera is seeking an experienced Oncology Registered Nurse (RN) to support the clinical review and validation of AI\-driven workflows that power Signatera reimbursement and coverage operations.
This role will serve as a clinical subject matter expert responsible for reviewing complex oncology cases, auditing AI\-generated clinical classifications, interpreting payer policy requirements, and providing feedback that improves the performance of Natera's clinical AI systems. The ideal candidate combines strong oncology expertise with a curiosity for technology and process improvement.
This position is highly collaborative and will work closely with Clinical Operations, Revenue Cycle Management, Product, Engineering, and Data Science teams to improve the accuracy, scalability, and effectiveness of AI\-assisted clinical review processes.
Primary Responsibilities
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### Clinical Case Review \& Escalations
- Review oncology patient records to determine whether cases meet clinical and payer policy requirements.
- Serve as an escalation point for complex cases that cannot be accurately classified by AI workflows.
- Analyze challenging clinical scenarios, including patients with multiple cancer diagnoses, complex treatment histories, and ambiguous documentation.
- Document findings and recommendations within internal systems including Salesforce, LIMS, and other workflow tools.
- Ensure accuracy and consistency of clinical review outcomes.
### AI Audit \& Validation
- Perform ongoing audits of AI\-generated clinical determinations and data classifications.
- Evaluate AI performance against established clinical review standards.
- Identify errors, trends, and opportunities to improve automation accuracy.
- Participate in validation activities for new AI models, workflows, and clinical review processes.
- Provide structured feedback to improve future AI performance.
### AI Development Support
- Partner with Product, Engineering, and Data Science teams to improve clinical AI capabilities.
- Help define clinical decision logic and requirements used in AI\-driven workflows.
- Translate clinical review findings into actionable recommendations for automation improvements.
- Support testing and implementation of new AI\-enabled solutions.
- Contribute to the long\-term evolution of Natera's oncology clinical intelligence platforms.
### Policy \& Coverage Expertise
- Develop expertise in payer coverage policies relevant to our Signatera product and oncology testing.
- Evaluate clinical documentation against coverage requirements.
- Assist in identifying cases that may require additional clinical review or reimbursement support.
- Stay current on evolving oncology standards, treatment pathways, and reimbursement trends.
Qualifications
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### Required
- Active RN license (state or compact).
- BSN preferred; ADN with significant oncology experience considered.
- 5\+ years of oncology nursing experience in inpatient, outpatient, infusion, navigation, utilization review, clinical review, case management, or related settings.
- Strong understanding of:
- + Cancer biology
+ Oncology treatment pathways
+ Disease staging
+ Patient treatment journeys
- Experience reviewing and interpreting medical records and clinical documentation.
- Strong critical thinking and clinical decision\-making skills.
- Ability to work independently in a fast\-paced, evolving environment.
- Excellent written communication and documentation skills.
- Comfortable working extensively within technology platforms and digital workflows.
### Highly Preferred
- Oncology Certified Nurse (OCN).
- Experience with payer policies, utilization management, prior authorization, or medical necessity review.
- Experience supporting AI, automation, clinical decision support systems, or technology\-enabled healthcare workflows.
- Experience using Salesforce, Epic, LIMS, or other healthcare technology platforms.
- Familiarity with molecular diagnostics, precision medicine, or MRD testing.
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
Salary Context
This $82K-$103K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($92K) sits 49% below the category median. Disclosed range: $82K to $103K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Natera AI Hiring
Natera has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Austin, TX, US, San Carlos, CA, US, Remote, US. Compensation range: $103K - $212K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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