Director, Machine Learning, Virtual Cell Initiative

Arc Institute; Palo Alto (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

Arc Institute, located in Palo Alto, CA, is seeking a Director of Machine Learning to lead innovative projects within the Virtual Cell Initiative. The ideal candidate will have a PhD and at least five years of experience in machine learning, alongside strong skills in leadership and communication. You will work collaboratively with biologists and engineers to develop advanced machine learning models aimed at revolutionizing drug discovery and understanding cellular functions. This role offers the opportunity to shape groundbreaking research in an inclusive and dynamic environment.

Qualifications

• Minimum of five years of experience in machine learning.
• Proven experience leading research teams.
• Excellent communication skills, written and verbal.

Responsibilities

• Lead a team of six ML research scientists and engineers.
• Collaborate with wet-lab scientists for data shaping.
• Build models for understanding cellular responses.

Skills

Machine learning

Biology

Single-cell genomics

Leadership

Communication

Education

PhD in Computational Biology, Bioinformatics, Machine Learning, or related field

Tools

PyTorch

TensorFlow

JAX

Job description

Director, Machine Learning, Virtual Cell Initiative

About Arc Institute

Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.

Unlike academia, our scientists have long‑term funding and industry‑like resources. Unlike industry, they are free to pursue high‑risk, long‑term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.

Our two Institute Initiatives reflect this model in action:

• Virtual Cell Initiative: Building a full‑stack virtual cell model to identify disease mechanisms and nominate drug targets, accelerating the path from biological insight to clinical trials.
• Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.

More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long‑term philanthropic funding.

Why this position could be the best job you’ve ever had?

• Work at the center of AI×Bio with the potential to revolutionize drug discovery for the world.
• Work in a unique environment that incorporates both wet lab data generation and frontier AI modelling in an active learning loop.
• Join a new type of research org that fuses high‑velocity execution of a startup with the intellectual rigor of a world‑class academic institute, with a long runway to tackle some of the hardest—and highest potential—challenges in science today.
• Collaborate with some of the most accomplished scientists and entrepreneurs in the world.

About the position

We are searching for an innovative scientific leader experienced in building predictive models based on single‑cell genomic data. The chosen candidate will spearhead the development and application of advanced machine‑learning models tailored for perturbative gene expression modeling, in the context of Arc’s virtual cell initiative.

About you

• You are passionate about machine learning, ideally with experience or strong interest in biology and single‑cell genomics.
• You want to develop highly innovative and accurate biology‑inspired multimodal machine‑learning models.
• You are excited about collaborating with a multidisciplinary team of computational and experimental biologists at Arc.
• You are a strong communicator, capable of translating complex technical concepts at the intersection of machine learning and biology.
• You are a continuous learner.
• You are interested in recruiting and managing your own group of scientists and engineers as well as mentoring and training other scientists.

In this position you will

• Lead and build a team of six ML research scientists and engineers, supplemented by undergraduate, master’s, and PhD students to contribute to the development of a state‑of‑the‑art foundation model and agentic framework for understanding how cells respond to perturbations.
• Work in an active learning loop with Arc’s wet‑lab scientists to shape the world's largest and most diverse set of single‑cell training data across many cell contexts.
• Collaborate closely with other research groups to integrate genomics, functional track, and omics data more broadly beyond scRNA‑seq data and Perturb‑seq.
• Stay up to date on the latest frontier ML research and pioneer new architectures and approaches.
• The ultimate goal is to build a high‑utility virtual cell model for use by biologists worldwide. We publish our breakthroughs to widely accelerate scientific progress and partner with some of the biggest names in AI.
• Commit to a collaborative and inclusive team environment, sharing expertise and mentoring others.
• Attract the very best talent in the world to support VCI initiative goals.

Requirements

• PhD in Computational Biology, Bioinformatics, Machine Learning, or a related field.
• Minimum of five years of experience working in or with machine learning, well versed in frameworks such as PyTorch, TensorFlow, JAX, etc.
• Proven experience leading research teams in a fast‑paced, multidisciplinary environment.
• Experience with or strong interest in biology with the ability to communicate and collaborate successfully with biologists and pure ML engineers.
• Excellent communication skills, both written and verbal, with a strong track record of presentations and publications.

Equal Employment Opportunity

As set forth in Arc Institute’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.