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About This Role
Role Overview
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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 (R\&D, 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
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Platform Development
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- 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
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- 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
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- 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
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- 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
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- 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, distributed systems, and agentic AI infrastructure concepts
- Influence cross\-functional roadmaps by partnering with Product and Engineering leadership to align delivery with business needs
Qualifications
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Required:
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- 8\+ years in software/ML engineering, with 5\+ years in ML engineering at scale
- Expertise in building production\-grade ML/LLM systems on AWS tech stack (Python, TensorFlow/PyTorch, Spark, MLflow/Kubeflow, vector DBs)
- Proven track record with GenAI/LLMs: fine\-tuning, RAG, prompt orchestration, agentic systems, safety guardrails, monitoring, and cost optimization
- Hands\-on with RAG systems (embeddings, vector DBs, retrieval policies) and LLM runtime operations (caching, quotas, multi\-model routing)
- Experience building agentic AI platforms (LangChain, LlamaIndex, CrewAI, Semantic Kernel, or custom)
- Deep knowledge of data\-intensive systems, distributed architectures, and cloud\-native development
- Strong grounding in compliance\-first engineering in healthcare, biotech, or diagnostics preferred
- Track record building secure, compliant data/AI systems and automating policy checks.
- Excellent ability to influence across teams, mentor engineers, and set technical standards
Preferred:
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- Masters degree 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)
The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \& depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.
Remote USA
$125,000 \- $156,300 USD
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.
Salary Context
This $125K-$156K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 21% below the category median. Disclosed range: $125K to $156K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Natera AI Hiring
Natera has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Remote, US. Compensation range: $132K - $233K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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