Machine Learning Research Scientist

Machine Learning Research Scientist

Remote, United States | South San Francisco, CA

Overview

An AI-first biotechnology company is advancing precision oncology through proprietary multimodal data and foundation models. Machine learning sits at the center of the company's scientific strategy, supported by one of the industry's largest proprietary multimodal oncology datasets combining deep spatial profiling with routine clinical assays.

The organization generates data purpose-built for machine learning, trains foundation models from scratch, and applies advanced research directly to drug discovery and therapeutic development.

The Opportunity

The Machine Learning Scientist will conduct original research and contribute to the development of next-generation biological foundation models.

Success in this position requires strong scientific judgment, deep machine learning expertise, and the ability to independently move from an initial research question through model development, experimentation, evaluation, and conclusion. The position is an individual contributor research role with an emphasis on scientific rigor and intellectual contribution rather than production software engineering.

Responsibilities

• Design, implement, and train foundation models across large-scale multimodal biological datasets

• Develop novel approaches for integrating information across biological scales and measurement modalities

• Explore advanced approaches across self-supervised learning, representation learning, multimodal learning, and generative modeling

• Identify meaningful benchmark tasks and design rigorous evaluation frameworks

• Rapidly prototype research ideas and prioritize high-value experiments

• Own research projects from initial concept through experimentation, analysis, and conclusion

• Evaluate emerging technologies, including large language models and agentic systems, for scientific research workflows

• Collaborate with machine learning researchers, computational scientists, biologists, and other domain experts

• Communicate research findings across technical and scientific audiences

• Contribute to publications, conference presentations, and broader scientific engagement

Areas of Interest

• Foundation Models

• Self-Supervised Learning

• Representation Learning

• Computer Vision

• Multimodal Learning

• Large Language Models

• Generative Modeling

• Diffusion Models

• Flow Matching

• Autoregressive Models

• Scientific Machine Learning

Qualifications

• Demonstrated success conducting original machine learning research in a rigorous academic or industry environment

• Strong publication record at leading machine learning conferences or evidence of significant research contributions within a respected industry research organization

• Experience writing model architecture code and datasets in PyTorch, including model training and optimization

• Deep knowledge of modern machine learning architectures and self-supervised learning approaches

• Ability to independently formulate research ideas, build models, design evaluations, analyze results, and iterate based on findings

• Strong research coding skills with the ability to develop robust implementations beyond traditional academic prototypes

• PhD in Machine Learning, Computer Science, Artificial Intelligence, Statistics, Applied Mathematics, Computational Neuroscience, Physics, or another highly quantitative discipline strongly preferred

Preferred Background

Experience in one or more of the following areas is valuable but not required:

• Computational Biology

• Genomics

• Drug Discovery

• Molecular