Machine Learning Scientist, BioML

About the role

Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date.

We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into models at the intersection of complex protein biology and AI. This position offers an opportunity to work at the forefront of generative modeling research across language processing, representation learning, and protein engineering. You should be a self-directed researcher who has the ability to rapidly prototype and evaluate new models and algorithms in the biomolecular domain.

As an early employee, you will proactively shape the direction of our machine learning efforts and collaborate across diverse teams of computational and experimental scientists.

Responsibilities

- Design and develop state-of-the-art predictive and generative models incorporating domain-specific evolutionary and experimental data

- Leverage massive-scale protein and nucleic acid data to train specialized models for protein understanding and design

- Curate relevant datasets and design tasks for rigorous evaluation of generative models

- Collaborate across the machine learning and protein design teams to adapt and apply techniques for experimental validation

- Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings

- Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company

Qualifications

- PhD (or equivalent industry experience) in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field

- Experience with conceiving of, implementing, and evaluating novel machine learning techniques at the intersection with biology

- Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS)

- Experience with modern deep learning frameworks such as Pytorch or Jax

Preferences

- Familiarity with foundational biology of proteins and nucleic acids

- Experience developing machine learning models for proteins (language models, structure prediction, design)

- Experience with cloud compute platforms (GCP, AWS, Azure, OCI)

- Previous experience in data extraction and curation from bioinformatics data sources

- Familiarity with wet lab experimental assays and associated limitations