Senior Machine Learning Research Scientist

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The Arc Institute is a new scientific institution that conducts curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.

While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:

• Funding: Arc will fully fund Core Investigators’ (PIs’) research groups, liberating scientists from the typical constraints of project-based external grants.
• Technology: Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators.
• Support: Arc aims to provide first-class support—operationally, financially and scientifically—that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction.
• Culture: We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration.

Arc has scaled to nearly 200 people. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years.

About the position

We are looking for an exceptional experienced machine learning scientist specializing in developing large-scale models. The ideal candidate will play a key role in constructing advanced machine learning models to predict cell state response to perturbations as part of Arc’s Virtual Cell Initiative.

About you

• You are passionate about machine learning with real-world applications and scientific impact.
• You want to develop cutting-edge, biology-inspired, multimodal machine learning models.
• You are excited about collaborating with a multidisciplinary team of experimental biologists and machine learning engineers at Arc.
• You are a strong communicator, capable of translating complex technical concepts to non-technical audiences across disciplines
• You are a continuous learner

In this position, you will

• Build state-of-the-art AI models for understanding how cells respond to perturbation, in collaboration with other ML researchers, engineers and experimental scientists at Arc.
• Guide interdisciplinary teams towards successful delivery of reliable and robust machine learning models with clear scientific impact.
• Stay up-to-date with the latest advancements in machine learning for computational biology, scope out new areas of work and ensure the models built at Arc remain state-of-the-art.
• Develop strategies for both training of models as well as the large-scale generation of new experimental data to train those models, as part of Arc’s Virtual Cell Initiative.
• Publish findings through journal publications, white papers, and presentations.
• Commit to a collaborative and inclusive team environment, sharing expertise, mentoring others and fostering collaborations.

Job Requirements

• PhD in Computer Science, Computational Biology, Bioinformatics, Machine Learning, or a related field.
• Minimum of 3 years of experience post-PhD in building machine learning models for large datasets.
• Strong research background with contributions to machine learning conferences (e.g., NeurIPS, ICLR, ICML) or interdisciplinary scientific journals (e.g., Nature, Nature Methods, Science).
• Well-versed in machine learning frameworks such as PyTorch.
• Excellent communication skills, both written and verbal, with a strong track record of publications.
• Proven track record of communicating and collaborating with domain experts and ML engineers.
• Motivated to work in a fast-paced, ambitious, multi-disciplinary, and highly collaborative research environment.

Preferred Qualifications

• Experience with training and scaling large-scale machine learning models.
• Strong foundation in software engineering with a proven ability to deliver machine learning products from research to production.
• Experience working with biological datasets including single-cell genomics, genomic sequences, bioimaging
• Demonstrated key contributions to the field of predictive modeling of cell states.

The base salary range for this position is $235,000-$291,000.