Arc Institute is looking for a machine learning scientist to develop large-scale models for predicting cell state response to perturbations as part of Arc’s Virtual Cell Initiative.
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.
- 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
Responsibilities
- 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.
Other
- 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
