Senior Machine Learning Scientist, Agentic AI

Natera

1d•$169,800 - $212,300•Remote

About The Position

Natera is seeking a Senior Machine Learning Scientist to join our AI team, an advanced R&D and core AI innovation team bridging the gap between molecular discovery and clinical execution. Leveraging a proprietary data moat of over 250,000 oncology patients profiled with longitudinal ctDNA, WES/WGS, digital pathology, and EMR data, you will design and deploy production-grade autonomous AI agents and multi-modal foundation models. Your mission is to architect systems capable of multi-step biological reasoning, converting complex multi-omic datasets into verifiable clinical insights that accelerate biomarker and therapeutic discovery. You will lead the next evolution of our Agentic AI platform, designing autonomous systems capable of reasoning through the complexities of cancer biology, orchestrating proprietary foundation models, and simulating virtual patient trajectories.

Requirements

PhD or Master's degree in Computer Science, Bioinformatics, Statistics, or a related quantitative field
8 or more years of experience in AI research or engineering, with a proven track record of moving multi-agent orchestration architectures or large-scale language model workflows from prototype to production
Deep experience with agentic frameworks, such as LangChain or Claude Agent SDK, retrieval-augmented generation (RAG), and validation frameworks for autonomous AI agents
Strong understanding of cancer genomics (WES/WTS), mutational signatures, and structure-activity relationships
Advanced production-level development experience using PyTorch and experience with distributed training on large GPU clusters, including NVIDIA H100s

Nice To Haves

Ability to operate with absolute ownership to close operational gaps and independently drive architectural deployment
Data-driven decision-making focused on empirical model performance and clinical validity
Technical leadership capability to define long-term AI engineering roadmaps
Rigor in code architecture, reproducibility, and production-grade software engineering practices
Comfort with high intellectual friction and the ability to defend scientific and engineering choices under rigorous internal peer review
Focus on translating machine learning outcomes directly into patient-centric clinical utility

Responsibilities

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