Machine Learning Scientist

Harnham; United States; On-site; USD 250,000 - 288,000; Full time

Job summary

Harnham is seeking a Machine Learning Scientist for an AI-first biotechnology company advancing precision oncology through proprietary multimodal data and foundation models. This is an individual contributor role emphasizing scientific rigor over production software engineering.

You will design, train, and evaluate large-scale models, collaborate with biologists and ML researchers, and contribute to publications and conferences. Remote work within the United States is supported with equity.

Qualifications

• Original ML research in academic or industry setting.
• Strong publication record at ML conferences.
• Experience writing model architecture code and datasets in PyTorch.
• Deep knowledge of modern ML architectures and self-supervised learning.
• Ability to independently formulate research ideas and iterate.
• Strong research coding skills beyond prototypes.
• PhD strongly preferred in a quantitative field.

Responsibilities

• Design, implement, and train foundation models on large-scale multimodal biological datasets.
• Develop approaches for integrating information across biological scales and measurement modalities.
• Explore self-supervised, representation, multimodal, and generative modeling.
• Identify meaningful benchmark tasks and design evaluation frameworks.
• Prototype research ideas and prioritize high-value experiments.
• Own research projects from concept through experimentation and conclusion.
• Evaluate emerging technologies for scientific workflows.
• Collaborate with ML researchers, computational scientists, biologists, and other domain experts.
• Communicate research findings across technical and scientific audiences.
• Contribute to publications and conference presentations.

Skills

ML research

Self-supervised

Multimodal learning

PyTorch

Education

PhD

Tools

Python

Model training

Job description

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

• Self-Supervised Learning


• Autoregressive Models

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


• Biochemistry

Biology experience is not required. Machine learning research excellence remains the primary hiring criterion. Relevant backgrounds may include computer vision, foundation models, language models, robotics, autonomous driving, reinforcement learning, and other advanced machine learning disciplines.

Compensation

Base Salary: $250,000–$288,000+, depending on experience and research background

Additional Compensation: Equity

Location & Work Authorization

The position may be performed fully remotely within the United States. Optional office access is available in South San Francisco.

Quarterly co-working weeks bring the broader team together in South San Francisco for dedicated research, collaboration, and planning.

Candidates must be authorized to work in the United States on a permanent basis. Employment sponsorship is not available for this position. Applicants must be U.S. citizens or lawful permanent residents (Green Card holders).