At a Glance
- Tasks: Optimise and deploy large-scale machine learning models for cutting-edge molecular modelling tools.
- Company: Join Boltz, a public benefit company revolutionising AI in biology and drug discovery.
- Benefits: Competitive salary, equity ownership, and the chance to make a real-world impact.
- Other info: Enjoy significant ownership and independence in a dynamic, impactful environment.
- Why this job: Be part of a talented team driving innovation in scientific research and technology.
- Qualifications: 5+ years in MLOps, strong experience with ML models, and solid software engineering skills.
The predicted salary is between 70000 - 90000 £ per year.
About Boltz
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.
About the role
As an MLOps Engineer, you will focus on optimizing, deploying, and operating large-scale machine learning models that power Boltz Lab. Your primary responsibility will be to ensure that advanced models for molecular modeling and design run efficiently, reliably, and cost-effectively across distributed systems.
You will work closely with ML Researchers to take trained models and turn them into production-ready services by optimizing training and inference performance, reducing memory and compute overhead, and scaling workloads across multi-GPU and cloud environments. This includes profiling, improving model throughput and latency and hardening systems for long-running and high-volume workloads.
This role is ideal for someone who thrives on technical ownership and operational excellence, enjoys working close to systems and infrastructure, and is motivated by deploying high-impact machine learning systems at scale for real-world scientific use.
About you
Essentials:
- 5+ years of experience in industry
- Strong experience deploying and operating machine learning models in production environments.
- Proven ability to optimize training and inference workloads, including profiling performance, reducing memory and compute usage, and improving throughput and latency.
- Hands-on experience with distributed frameworks and tooling
- Hands-on experience with PyTorch and the scientific Python ecosystem.
- Strong understanding of MLOps best practices, including experiment tracking, model versioning, reproducibility, and CI/CD for ML systems.
- Strong software engineering fundamentals, with experience building reliable, well-tested, and maintainable ML infrastructure.
- Comfortable collaborating closely with ML researchers to translate research models into robust production services.
Nice to have:
- Exposure to computational biology or chemistry workflows and data formats.
- Background working with large-scale scientific or numerical workloads.
- Experience operating ML systems under real-world constraints such as cost, latency, and reliability.
What we offer
- 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.
MLOps Engineer in London employer: Boltz
Boltz is an exceptional employer for MLOps Engineers, offering a unique opportunity to work at the forefront of AI-powered molecular modeling tools that significantly impact drug discovery. With a strong emphasis on collaboration, innovation, and technical ownership, employees enjoy a vibrant work culture that fosters professional growth and independence. The competitive salary and substantial equity ownership further enhance the appeal of joining a mission-driven company dedicated to making advanced scientific capabilities accessible to all.
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We think this is how you could land MLOps Engineer in London
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We think you need these skills to ace MLOps Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Boltz, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Boltz. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Boltz
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
