Engineering · Palo Alto, US
About Arc Institute
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:
Arc scaled to nearly 100 people in its first year. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly to several hundred in the coming years.
About the position
We are looking for an exceptional 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.
- Stay up-to-date with the latest advancements in machine learning for computational biology and ensure the models built at Arc remain state-of-the-art.
- Guide both the 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.
- Collaborate with experimental biologists to ensure that the developed models are grounded in biologically impactful use cases.
- Publish findings through journal publications, white papers, and presentations (both internal to Arc and external).
- 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 in building machine learning models for large datasets.
- Well-versed in machine learning frameworks such as PyTorch.
- Excellent communication skills, both written and verbal, with a strong track record of publications.
- Ability to communicate and collaborate successfully with domain experts and ML engineers.
- Motivated to work in a fast-paced, ambitious, multi-disciplinary, and highly collaborative research environment.
Preferred Qualifications
- Experience working with biological datasets including single-cell genomics, genomic sequences, bioimaging
- Knowledge of cell and molecular biology
- Experience processing sequencing data from biological experiments
- Software engineering experience
- Demonstrated key contributions to the field of predictive modeling of cell states.
The base salary range for this position is $143,500 to $202,550. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.