ML Scientist

INFO SALARY

$200000 - $280000 LOCATION San Francisco

JOB TYPE

Permanent ML Scientist / Researcher Oncology AI • Foundation Models • Life Sciences Remote About the Role We are building foundation models trained on human tumor biology - one of the most consequential and technically demanding challenges at the intersection of AI and medicine. As an ML Scientist, you will be a core research contributor designing and training these models across multimodal omics datasets, partnering closely with biologists and fellow research scientists to advance the state of the art in oncology AI. This is a research-forward role for scientists who want their work to matter. We are looking for people with a track record of research excellence - those who have gone deep on model architecture, training dynamics, and rigorous experimental design. If you have built models from the ground up and published findings, we want to talk. What You'll Do Design and train large-scale foundation models on multimodal biological datasets, including genomics, transcriptomics, and other omics modalities Collaborate deeply with computational biologists, research scientists, and domain experts to translate biological questions into tractable modeling problems Drive the full research lifecycle: hypothesis formation, experimental design, model development, and rigorous analysis of results Contribute to agentic AI systems that reason over complex biological data Communicate findings internally and, where appropriate, through peer-reviewed publication What We're Looking For Must-Haves Strong research background, typically evidenced by a PhD in machine learning, computational biology, statistics, physics, or a related quantitative field - or equivalent industry research experience Demonstrated ability to build and train models end-to-end, including experimental analysis and iteration Research excellence: first-author publications at top ML, AI, or computational biology venues are a strong positive signal Deep familiarity with foundation model concepts: pretraining, self-supervised learning, attention mechanisms, and large-scale training Comfort working at the intersection of biology and machine learning - even without a formal biology degree Nice-to-Haves Experience with biological or omics data (genomics, proteomics, pathology imaging, etc.) Prior work in multimodal learning or multi-omics integration Familiarity with agentic AI systems or tool-use frameworks Background in oncology or disease biology What This Role Is Not This is not a production ML engineering or MLOps role. We are not looking for candidates whose primary experience is model deployment, serving infrastructure, or engineering-heavy systems work. The emphasis here is firmly on research depth and model development.

Compensation & Location Base Salary:

$250,000 - $288,000 (depending on experience) + equity

Location:

Remote-friendly; office in South San Francisco, CA CONTACT Tim Lucas Manager