Staff Machine Learning Scientist, Translational AI

POSITION SUMMARY:

We are seeking a Staff Machine Learning Scientist – Translational AI to provide technical leadership at the intersection of deep learning foundation models, computational biology, and molecular diagnostics. This ownership role drives the architecture and validation of genomic, transcriptomic, and multimodal sequence models to accelerate patient stratification, target identification, and therapeutic monitoring across our cell-free DNA (cfDNA) and multi-omic platforms. This Staff-level position operates with broad technical autonomy, driving modeling strategy across multiple concurrent portfolios while maintaining direct execution responsibilities in model compilation, scaling, and testing. Working within a builder framework, you will align across AI Research, Bioinformatics, and Clinical Science divisions to transition advanced representation learning models into reproducible, clinically valid diagnostic assets.

PRIMARY RESPONSIBILITIES:

Scientific Leadership in Translational AI

Serve as the principal technical authority on the deployment of molecular, genomic, and pathology foundation models applied to oncology and translational medicine questions

Engineer rigorous alignment and post-training workflows that ground pre-trained foundation models in empirical clinical trial and molecular diagnostic data, eliminating speculative modeling assumptions

Formulate objective peer-review frameworks and deliver technical feedback to elevate the modeling code, experimental standards, and scientific designs of the broader AI research group

Foundation Models to Biological and Clinical Translation

Lead the post-training, parameter-efficient fine-tuning (PEFT), and evaluation of deep sequence, multimodal, and representation learning models for biomarker discovery, molecular recurrence monitoring, and therapeutic response forecasting

Design robust fine-tuning, probing, and latent space representation analysis workflows that extract interpretable, biologically grounded patterns from high-dimensional transformer architectures

Validate model outputs against multi-omic benchmarks and real-world outcomes, ensuring model predictions deliver the exact deterministic accuracy required for patient tracking and clinical interventions

Modeling, Experimentation, and Evaluation

Build, train, and optimize advanced machine learning models utilizing next-generation sequencing (NGS), ctDNA assays, digital pathology imaging, and longitudinal clinical metadata

Design rigorous clinical investigation and evaluation frameworks that connect model performance metrics (e.g., loss curves, precision-recall) directly to translational utility and real-world distribution shifts

Systematically identify algorithmic failure modes, sources of dataset bias, and covariate shift, implementing robust mitigation strategies suitable for regulated, clinical-facing pipelines

Cross-Functional Collaboration and Influence

Partner with Computational Biology, Translational Science, and Medical Affairs teams to translate complex clinical requirements into clear, quantitative machine learning problem statements

Act as a systems-level technical bridge between AI Research and ML Engineering teams to ensure that validation models convert seamlessly into scalable, reproducible production workflows

Provide technical leadership and data execution support for strategic external collaborations, pharmaceutical partnerships, and foundation model research consortiums

Scientific Communication and External Presence

Translate complex multimodal model architectures and performance metrics into transparent, high-integrity data packages for clinical governance, leadership updates, and external collaborators

Lead the authoring of technical manuscripts for peer-reviewed machine learning venues (e.g., NeurIPS, ICML, ICLR) and major computational biology journals

Act as a technical representative for the company's translational AI capabilities at international medical, oncology, and machine learning conferences

QUALIFICATIONS:

PhD in Computer Science, Computational Biology, Bioinformatics, Biomedical Engineering, or a highly quantitative structural field

5+ years of industry or post-doctoral experience applying deep learning frameworks to complex biological, genomic, or clinical datasets, with a documented focus on oncology or immunology portfolios

Deep technical competency with transformer architectures, representation learning, self-supervised learning (SSL), or deep sequence modeling

Proven track record of translating machine learning outputs into verifiable biological variables or clinical performance indicators, rather than optimizing solely for isolated cross-validation metrics

Expert proficiency in PyTorch and modern machine learning infrastructure (e.g., HuggingFace ecosystem, PEFT, Captum, MLflow, and distributed GPU computing setups)

Documented technical leadership through end-to-end project ownership, architectural design authority, or cross-functional team direction

Preferred Qualifications:

Experience constructing or fine-tuning multimodal foundation models that combine high-depth genomic sequencing data with digital pathology images or longitudinal electronic health records (EHR)

Direct experience handling clinical trial datasets, real-world data (RWD/RWE), or developing models within health-authority/regulatory-facing frameworks

Strong record of publications as primary author in high-impact machine learning venues

KNOWLEDGE, SKILLS, AND ABILITIES:

Advanced mathematical and algorithmic fluency across deep learning methodologies, optimization strategies, and probabilistic modeling

Fast learner with the capability to master complex cfDNA platforms, biochemistry workflows, and multi-omic data generation pipelines rapidly

Precise written and verbal communication styles with strict attention to algorithmic detail and statistical validation boundaries

Proven capability to drive independent portfolios while executing cross-functional objectives within matrixed technology and scientific teams

High-growth builder mindset with the capability to balance scientific rigor, operational execution speed, and computational resource constraints under tight timelines

Utilize cloud-based productivity and high-performance computing infrastructure to maintain high operational momentum in a fast-evolving artificial intelligence environment

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

$163,200 — $220,000 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