ML Research Engineer/Scientist at Boltz (London, United Kingdom)ML Research Engineer/Scientist

Boltz is a public benefit company building the next generation of AI-powered molecular modeling tools to make biology programmable and accelerate drug discovery, while keeping frontier capabilities broadly accessible. Boltz-1, Boltz-2, and BoltzGen are open models trusted by 100,000+ scientists across biotech and academia, and used in programs at every Top 20 pharma as well as leading agrichemical and industrial research organizations. We deliver these capabilities through Boltz Lab, our platform for running our latest models and design agents as reliable, production-grade tools. Boltz Lab is designed around real chemistry and biology workflows, so teams can start from a target and a hypothesis and quickly generate, evaluate, and rank candidate molecules. We provide the compute, the scalable infrastructure, and the collaboration layer, so scientists can iterate faster and stay focused. You can read more about our mission, research and product vision on our manifesto.

As an ML Research Engineer/Scientist, you will develop the next generation of machine learning models and algorithms that power Boltz Lab and expand what scientists can do with AI in molecular modeling and design. You’ll collaborate closely with an interdisciplinary team of ML researchers, domain experts in chemistry and biology, and software engineers. Together, you’ll design new model architectures, training objectives, and the high-quality datasets required to train them, pushing performance on fundamental tasks in drug discovery. You’ll also help define how we evaluate progress, building rigorous benchmarks and validation pipelines that connect offline metrics to real-world outcomes. This role is for someone who is a mission-driven technical leader who wants to push the frontier and then turn that progress into capabilities that thousands of scientists can use. You’ll set direction as well as execute while holding a high bar on scientific rigor, strong engineering practices and real-world impact.

Essentials:

• Extensive publication record in top-tier ML or life-science conferences and journals (NeurIPS, ICLR, ICML, Nature Methods, and related).
• Demonstrated strength in deep learning research and development, including designing new architectures, running rigorous experiments, and performing careful analysis.
• Strong hands-on experience with PyTorch and the scientific Python ecosystem (NumPy, SciPy, Pandas, etc.).
• Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.

Nice to have:

• Experience training, scaling, and evaluating large models on applied, real-world problems, including building reliable evaluation suites and diagnosing failure modes.
• Experience working in an interdisciplinary scientific environment, especially across ML, biology, chemistry, and physics.
• Familiarity with the tools, data formats and workflows commonly used in computational biology and chemistry.

• Opportunity to drive outsized real-world impact by building tools that empower thousands of scientists across the industry.
• Work alongside one of the most talent-dense teams in the field.
• Significant ownership and independence, with responsibility for driving projects from concept to deployment.
• Highly competitive salary with substantial equity ownership.