Senior/Staff Machine Learning Scientist, Computational Pathology

• Own AI biomarker end to end, from problem framing with our product, medical, and biostatistics partners through validation, regulatory submission, and production.
• Lead the next generation of our pan-cancer self-supervised pathology foundation models and the downstream models built on them (multiple-instance learning, time-to-event, segmentation), with generalization as a first-order objective.
• Own the reproducibility and robustness of model predictions across scanners, institutions, staining protocols, and patient populations.
• Manage coding agents to propose and execute experiments, improve the efficiency of our tooling, and identify biological insights that explain our models’ behavior.
• Serve as technical lead: set technical strategy, break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risks early, and hold submission and launch dates.
• Present findings to product, medical, and biostatistics teams, author and defend regulatory and quality documentation, and represent AI in design and development reviews to get models into patients’ hands.
• Mentor ML scientists and engineers, and raise the bar on scientific rigor, code quality, and written communication across the team.
• Publish in peer-reviewed journals, present at clinical and ML venues, and support external academic and industry collaborations.

You're an experienced ML scientist or engineer who has taken models from research into clinical or production use and has led technical programs through to launch. You bring real depth in oncology or biomarker development.

• 5+ years of industry experience building deep learning systems (we use PyTorch)
• Hands-on experience with coding agents like Cursor or Claude Code
• A track record of taking models beyond a notebook, including launching and monitoring ML products in production, and evaluating them rigorously on messy, real-world data
• 2+ years as a technical lead for complex ML programs: scoping problems, setting technical direction, sequencing work, and managing dependencies and risk across teams on dated deliverables
• Demonstrated depth in oncology or biomarker development: cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable
• Proven ability to communicate complex ML concepts to cross-functional, non-ML collaborators, including product, medical, and biostatistics teams
• Experience mentoring or managing ML scientists and engineers

If you meet most of this and the work excites you, we'd love to hear from you.

Nice to have: ML on complex clinical data (medical imaging, multi-omics, longitudinal records); weakly supervised learning (e.g., MIL) and survival analysis; self-supervised representation learning (e.g., DINOv3) and adapting medical foundation models to clinical tasks; ML in regulated environments (FDA 510(k)/De Novo, SaMD, design controls, CLIA/LDT); cloud-scale training and orchestration (e.g., Flyte/Union, AWS).

You don't need: a PhD. Prior regulatory experience is a strong plus, and we'll help you build on it here.

How you'll grow: ownership of a biomarker program across the full lifecycle of a regulated clinical ML product, a direct hand in shaping our technical strategy and roadmap, and room to grow into broader technical leadership across the AI team.

• Your models help cancer patients. This isn’t AI for generating cat videos or customized emojis. Our work directly affects patients who are experiencing among the most difficult periods of their lives.
• Foundation-model scale. We train our own pathology foundation models from scratch with distributed multi-node GPU training on AWS. While frontier labs may have cracked coding and math, their ability to read biology for our problems is unusable. Artera is working on the cutting edge of this space.

About Us: Artera is an artificial intelligence company dedicated to transforming cancer care. We’ve developed foundation models that analyze clinical and pathology data, generating actionable insights that guide therapy selection and improve outcomes for cancer patients. By continuously improving these models, we aim to uncover the biological mechanisms driving cancer progression.

Build ML that changes how cancer is treated.

Most medical AI stops with a publication. Our AI models guide treatment decisions today.