Foundation and generative models for biomolecules

Inceptive; Zürich; Vor Ort; CHF 120.000 - 180.000; Vollzeit

Zusammenfassung

Inceptive is seeking a machine learning researcher to drive forward biomolecule design using generative models. You will join an antedisciplinary team including ML researchers, engineers, biologists, and data scientists to translate capabilities into therapeutic designs.

The role focuses on training, evaluating, and refining foundation models, designing experiments, and deploying tools for reproducible ML workflows. Travel for retreats and events is expected; office-based collaboration is valued.

Qualifikationen

• 3+ years of hands-on experience developing ML models.
• Proven track record of implementing and improving advanced ML models.
• Fluent Python + PyTorch or similar framework.
• Willingness to travel and collaborate across US and Europe.
• Preference for in-person collaboration at office locations.

Aufgaben

• Develop, train, and iteratively improve state-of-the-art biomolecule design models.
• Analyze, visualize, and communicate results to enhance models and data.
• Create and refine tools for ML experimentation and production.
• Collaborate with biologists to gather data for training and evaluation.
• Provide mentorship and technical direction to team members.

Kenntnisse

ML models

Python

PyTorch

Team collaboration

Ausbildung

PhD or equivalent experience

Tools

Compute clusters

Jobbeschreibung

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