Staff Machine Learning Scientist, Agentic AI

Natera is hiring a Staff Machine Learning Scientist, Agentic AI for a full-time US Remote role focused on building production autonomous agents and multi-modal models that turn complex omics data into clinical discoveries.

What This Role Involves

As Staff Machine Learning Scientist, Agentic AI at Natera you will sit inside an advanced research group that connects molecular findings to clinical use. Drawing on proprietary records from more than 250,000 oncology patients that include longitudinal ctDNA, WES/WGS, digital pathology, and EMR material, you will construct and release production autonomous agents together with foundation models able to perform successive biological reasoning steps. The work centers on systems that orchestrate specialized models, simulate patient trajectories, and convert multi-omic inputs into confirmable diagnostic and therapeutic outputs that accelerate biomarker and treatment discovery. You will also set the technical direction for the agentic platform used by biopharma and therapeutic discovery teams.

Responsibilities

• Direct design and launch of multi-agent architectures that independently generate hypotheses and invoke tools for tasks such as genomic variant calling, large-language-model fine-tuning, and clinical-trial matching
• Extend the company transformer foundation model by fusing DNA, RNA, and H&E imaging streams so agents can carry out multi-step biological reasoning and tool use
• Apply advanced language-model reasoning approaches including ReAct and Chain-of-Thought plus reinforcement fine-tuning so agents supply accurate and inspectable clinical justifications
• Create systems that autonomously map complex multi-modal inputs onto diagnostic and therapeutic conclusions while preserving human-traceable reasoning paths
• Own technical strategy and product roadmaps for agentic workflows across biopharma solutions and therapeutics discovery, turning clinical questions into scalable AI pipelines
• Define production machine-learning engineering standards and fully reproducible architectures that guarantee model transparency and scientific auditability
• Lead cross-functional alignment by rigorously defending agentic designs and biological reasoning methods in internal peer review

Who We're Looking For

• PhD or Master's degree in computer science, bioinformatics, statistics, or another quantitative discipline
• Eight or more years of AI research or engineering experience that includes taking multi-agent orchestration or large-scale language-model systems from prototype into production
• Deep practical knowledge of agent frameworks such as LangChain or Claude Agent SDK, retrieval-augmented generation, and validation methods for autonomous agents
• Solid grasp of cancer genomics (WES/WTS), mutational signatures, and structure-activity relationships
• Advanced production development with PyTorch and distributed training on large GPU clusters that include NVIDIA H100 hardware
• Demonstrated ownership that closes operational gaps and independently drives architectural delivery
• Data-centered judgment that prioritizes measured model performance and clinical validity
• Capability to set long-range AI engineering roadmaps and uphold rigorous code, reproducibility, and production software practices
• Comfort defending scientific and engineering choices under high-stakes peer scrutiny while keeping patient-centered clinical utility as the end goal

About Natera

Natera is a global leader in cell-free DNA testing dedicated to oncology, women's health, and organ health. Its mission is to make personalized genetic testing and diagnostics part of standard care so that earlier and more precise interventions can extend and improve lives. Team members from statistics, genetics, medicine, laboratory science, business, and software engineering work side by side in a setting known for rapid growth and high intellectual standards. For this US Remote Staff Machine Learning Scientist, Agentic AI opening the annual pay range is $163,200 to $220,000 USD; final offers reflect experience, skills, certifications, and related factors and may adjust for local labor costs.

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