Foundation and generative models for biomolecules

Inceptive; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Inceptive in San Francisco is advancing biomolecule design by training large-scale foundation models and generative approaches. You will work with machine learning researchers, engineers, biologists, and data teams to translate models into therapeutic designs.

The role emphasizes creating robust ML experimentation tools, collaborating across the US and Europe, and guiding junior teammates while delivering state-of-the-art biomolecule design solutions.

Qualifications

• 3+ years of hands-on experience developing ML models.
• Strong Python and PyTorch experience.
• Willingness to travel and work across US and Europe.
• In-person collaboration and preference to work from office locations.

Responsibilities

• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design.
• Analyze, visualize, and communicate results to support model improvements.
• Create, deploy, and refine tools for ML experimentation and production.
• Work with biologists to collect data for training and evaluation of generative models.
• Provide mentorship and technical direction to team members.
• Collaborate with ML researchers and engineers to advance models.

Skills

ML model development

Python programming

AA/ML experimentation

Education

PhD in AI/ML or related field

Tools

PyTorch

Python

GenAI frameworks

Job description

At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software.

The design space of biomolecules is unimaginably vast — far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.

We advance the state of the art in molecular design by training large-scale foundation models and developing cutting edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.

You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.

Your Mission, should you choose to accept it

• Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design
• Analyze, visualize, and communicate results to support team efforts in improving models and data
• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production
• Work with biologists to collect data for the training and evaluation of generative models of biomolecules
• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models
• Demonstrated track record of implementing, training, improving advanced machine learning models
• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework
• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET
• Readiness to travel several times a year for company retreats and business events
• We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations

Preferred technical skills

• PhD in AI/ML, computer science, computational biology, physics, or a related field, or equivalent practical experience (e.g., industry experience, research, or advanced technical expertise)
• Strong skills in designing, executing, and documenting machine learning experiments
• Practical experience with modern generative models
• Strong software engineering skills, in particular for data processing, evaluation of ML models, compute cluster orchestration
• Experience with large-scale model training, foundation models, model parallelism, multi-node training
• Experience with bio sequence data and datasets — various genomic and protein data, sequencing, functional assays, etc
• Knowledge of biochemistry, molecular/cell biology, and drug development