• Designing, building, and delivering highly scalable big data and ETL solutions across distributed environments. • Supporting projects at various and unanticipated client worksites throughout the United States. • Working closely with cross-functional teams to develop, test, and deploy data solutions that meet business and operational needs. • Leveraging modern big data frameworks, cloud platforms, and agile development practices. • Preparing technical design documentation for implemented solutions. • Identifying and resolving data pipeline issues and providing timely solutions.

Senior AI-Machine Learning Engineer US Remote 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 (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 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, 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 Required: - 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: - 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.

• Lead the technical design and deployment of multi-agent systems capable of autonomous hypothesis generation and tool use, including genomic variant calling, LLM fine-tuning, and clinical trial matching pipelines • Incorporate and advance Natera’s transformer-based foundation model by integrating DNA, RNA, and H&E imaging modalities for multi-step biological reasoning and tool use • Implement advanced LLM reasoning frameworks, such as ReAct and Chain-of-Thought, alongside reinforcement fine-tuning (RFT) to ensure agents provide accurate, explainable clinical rationales • Architect systems that autonomously translate complex, multi-modal data into diagnostic and therapeutic insights with human-verifiable reasoning and tracing • Own the technical strategy and product roadmap for agentic workflows across the Biopharma Solutions and Therapeutics Discovery division, converting complex clinical challenges into scalable AI systems • Establish production-grade machine learning engineering standards and reproducible architectures across the AI team to ensure absolute model transparency and scientific auditability • Drive cross-functional alignment and technical consensus by defending agentic architectures and biological reasoning frameworks in rigorous peer reviews

• Pipeline Automation: Design, implement, and manage automated CI/CD and Continuous Training (CT) pipelines for machine learning model development, evaluation, and delivery. • Model Deployment: Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows. • Observability & Monitoring: Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift. • Infrastructure Management: Provision and optimize cloud-based ML infrastructure (including GPU/CPU computing clusters) utilizing Infrastructure as Code (IaC) paradigms. • Cross-Functional Collaboration: Work intimately with product development teams to drive infrastructure adoption and efficiency gains through SDK/API development, automation and efficient ML system maintenance. • Governance & Compliance: Implement robust lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security across the entire ML lifecycle. • Data Infrastructure & Tooling: Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and ML (including streaming tools like Kafka and Flink for low-latency online inference). • Thought Leadership: Act as a mentor and thought leader, helping to define best practices in machine learning engineering, scalable ML service ops, and agentic AI (AI-Native) best practices.