Profluent is an AI-first protein design company. Founded in 2022, we develop deep generative models to design and validate novel, functional proteins to revolutionize biomedicine. Based in Berkeley, CA, we are backed by leading investors including Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures.

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)

Actual salary will be determined based on relevant skills, qualifications, experience, training, and market data. Benefits package may vary depending on company policies and eligibility criteria.

Hiring Salary Range$150,000$200,000 USD

What we offer at Profluent

  • A high-growth opportunity with meaningful impact
  • Competitive compensation package
  • Health insurance (health/dental/vision)
  • Generous paid time off (PTO) policy
  • Commitment to physical and mental well-being
  • More benefits and perks to be added!

Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law.

Legal authorization to work in the United States is required. In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.

Salary

$150,000 - $200,000

Yearly based

Location

Berkeley, California, United States

Job Overview
Job Posted:
3 months ago
Job Expires:
Job Type
Full Time

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