Machine Learning Engineer

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, experienced Machine Learning (ML) Engineer to drive the implementation, optimization and deployment of ML models for biomolecular design. This position offers an opportunity to work at the forefront of generative modeling research across language processing, geometric representation learning, and protein engineering. You should be an execution-focused self-starter with an eye for efficiency and scalability. 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

  • Optimize and deploy state-of-the-art deep learning models for protein sequences and structures
  • Develop efficient, high-quality code and data pipelines
  • Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings
  • Establish automated processes to continuously evaluate and improve our protein design methodology
  • 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

  • MS or PhD in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field
  • 2+ years of industry experience in machine learning infrastructure, pipeline building, distributed training, and deployment
  • Demonstrated ability in re-implementation of multiple state-of-the-art models from research for comparative analysis
  • Domain expertise in one or more of the following: language models, variational autoencoders, diffusion models, or graph neural networks
  • Ability to write clean, performant code and deploy services to cloud compute platforms (GCP, AWS, Azure)

Preferences (but not required)

  • Familiarity with foundational biology of proteins and nucleic acids
  • Experience with conceiving of, implementing, and developing novel machine learning methodologies
  • Familiarity with state-of-the-art machine learning models for proteins (large language models, AlphaFold, etc.)
  • Publications at major machine learning conferences or in major scientific journals

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
$140,000$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.

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logo Profulent Machine Learning Engineer Full-time On-site 📍 Berkeley, California, United States Apply Now
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