Applied ML Engineer/Scientist - Remote, Equity, Real-World Impact

Boltz; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Equity ownership

Job summary

Boltz is seeking an Applied ML Engineer/Scientist in London or remote to apply and adapt Boltz’s models to drug discovery challenges. You will curate datasets, select architectures, and deploy models for real-world use with external partners.

This role blends ML research with practical deployment in a fast-paced biotech context. Ideal candidates have hands-on ML in biology or chemistry, strong PyTorch experience, and a track record of delivering reliable, testable modelling solutions that scale

Qualifications

• Strong hands-on experience applying ML to real-world biology, chemistry, or drug discovery.
• Familiarity with workflows and data formats used in computational biology and chemistry.
• Proficient with PyTorch and the scientific Python ecosystem (NumPy, SciPy, Pandas).
• Experience contributing to deep-learning codebases with emphasis on reproducibility and testing.

Responsibilities

• Own end-to-end applied modeling loop from problem formulation to delivery.
• Adapt and fine-tune foundational ML models for drug discovery use cases.
• Collaborate with ML researchers, software engineers, and chemistry/biology experts.
• Identify gaps and improve models based on partner feedback.

Skills

Applied ML in biology

PyTorch

Python scientific stack (NumPy/SciPy/P

Reproducibility & testing

Tools

NumPy

SciPy

Pandas

Job description

Boltz is seeking an Applied ML Engineer/Scientist in London or remote to apply and adapt Boltz’s models to drug discovery challenges. You will curate datasets, select architectures, and deploy models for real-world use with external partners.

This role blends ML research with practical deployment in a fast-paced biotech context. Ideal candidates have hands-on ML in biology or chemistry, strong PyTorch experience, and a track record of delivering reliable, testable modelling solutions that scale