Machine Learning Scientist, Computational Pathology

Team: AI

Location: Remote-US

Commitment: Full-Time

Workplace Type: remote

Salary:

Why the work is different at Artera:

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.

What youll do:

Build and evaluate AI models that predict patient outcomes and treatment benefit from whole-slide images and clinical data, or infer molecular traits directly from slides.

Push the frontier in terms of our pan-cancer self-supervised pathology foundation models.

Manage coding agents to propose and execute experiments, improve the efficiency of our tooling, and identify biological insights that explain our models behavior.

Review findings with product, medical, and biostatistics teams, and contribute to regulatory documentation that gets models into patients’ hands.

Co-author papers and share your work at conferences.

What were looking for:

You might be a few years into industry or finishing graduate research. Either way, were looking for someone who has built ML that holds up on real data and has some grounding in biology or medicine.

1+ years building deep learning models (we use PyTorch), in industry or in substantial graduate research

Hands-on experience with coding agents like Cursor or Claude Code

Experience taking models beyond a notebook and evaluating them rigorously on messy, real-world data

A background in oncology, biology, or biomedical science, from coursework, research, or work (e.g., a biology or bioengineering degree, computational biology research, or ML work in healthcare or biotech)

Clear communication and an interest in working closely with product, medical, and biostatisticians

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

Nice to have: large-image computer vision or medical imaging; self-supervised learning; weakly supervised learning (e.g., MIL); survival analysis; domain shift and robustness.

You dont need: a PhD or regulatory experience. Youll learn the regulatory side here, and well deepen your oncology knowledge on the job.

How youll grow: close collaboration with senior ML scientists and engineers, experience across the full lifecycle of a regulated clinical ML product, and room to grow into leading a workstream.