Machine Learning Engineer

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