Product Software Engineer for ML-Driven Molecular Design

Boltz; England; On-site; GBP 65,000 - 90,000; Full time

Benefits offered by this job

Equity ownership

Job summary

Boltz is seeking a Software Engineer in Product to build the user-facing systems for Boltz Lab, enabling scientists to work with ML-driven molecular modeling tools.

You will design and ship product features that translate advanced machine-learning capabilities into clear, usable workflows, collaborating with designers, ML researchers, and domain scientists to shape the end-to-end experience.

Qualifications

• Strong experience building and shipping user-facing products in production environments.
• Solid software engineering fundamentals, with emphasis on clean abstractions, maintainability, and testing.
• Ability to collaborate with designers and product partners to translate user needs into thoughtful technical solutions.
• Experience owning features end-to-end, from initial design through implementation, iteration, and maintenance.
• Strong communication skills across engineering, product, and research teams.

Responsibilities

• Design and ship product features that translate advanced machine-learning and molecular modeling capabilities into clear, usable workflows.
• Work closely with product designers, ML researchers, and domain scientists to shape the end-to-end experience, from interfaces and APIs to application logic.
• Turn complex technical systems into elegant products that scientists rely on daily in drug-discovery workflows.
• Focus on usability and clarity, ensuring researchers can interact with and trust model-driven tools.

Skills

User-facing product shipping

Software engineering fundamentals

Collaborating with designers

End-to-end ownership

Strong communication

Tools

APIs

Job description

Boltz is seeking a Software Engineer in Product to build the user-facing systems for Boltz Lab, enabling scientists to work with ML-driven molecular modeling tools.

You will design and ship product features that translate advanced machine-learning capabilities into clear, usable workflows, collaborating with designers, ML researchers, and domain scientists to shape the end-to-end experience.