Machine Learning Scientist

Profluent is looking for a motivated, creative Machine Learning (ML) Scientist to drive research into new technologies for biomolecular design. 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 deep learning methods for protein sequence, structure, and function prediction and apply them to protein design


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


• Collaborate with Biology team to design and characterize novel designed biomolecules


• 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


• 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 (but not required)

• Familiarity with foundational biology of proteins and nucleic acids


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


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


• Familiarity with wet lab experimental assays and associated limitations


• Experience with cloud compute platforms (GCP, AWS, Azure)