Applied ML Engineer Scientist
Boltz PBC is an AI-for-science company building biomolecular models, compute infrastructure, APIs, and collaborative tools for molecular design and drug discovery.
Funding history
About Boltz
Boltz develops frontier AI models for biomolecular structure prediction, binding-affinity estimation, and protein and small-molecule design. Its current offerings include open-source models and the commercial Boltz Lab and Boltz API products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will adapt foundational machine-learning models to drug-discovery problems for external partners. You will curate datasets, tune model architectures and objectives, evaluate applied models, deliver solutions, and feed practical learnings and failure modes back into core model development.
Requirements
- Experience applying machine learning to real-world problems in biology, chemistry, or drug discovery
- Familiarity with computational biology and chemistry datasets, tools, data formats, and workflows
- Experience with PyTorch
- Experience with the scientific Python ecosystem including NumPy, SciPy, and Pandas
- Experience contributing to and maintaining deep-learning codebases
- Knowledge of engineering quality, reproducibility, and testing
Responsibilities
- Apply and adapt machine-learning models to drug-discovery problems
- Fine-tune, condition, and deploy models for scientific use cases
- Translate partner needs into modeling strategies
- Curate and adapt datasets
- Select and tune model architectures and objectives
- Own problem formulation, experimentation, evaluation, and delivery
- Identify practical gaps and failure modes and improve foundational models
Benefits
- Substantial equity ownership