At a Glance
- Tasks: Architect and develop cutting-edge AI systems for drug development.
- Company: Innovative biotech company on a mission to transform healthcare.
- Benefits: Competitive salary, flexible work hours, and opportunities for professional growth.
- Other info: Be part of a dynamic environment with a focus on innovation.
- Why this job: Join a pioneering team and make a real difference in drug development.
- Qualifications: 5+ years of experience in ML systems and expertise in PyTorch.
The predicted salary is between 63000 - 77000 £ per year.
We are on a mission to redefine drug development by building the first foundation model of microbiome-drug interactions. We’re looking for a founding Principal Machine Learning Engineer, a hands-on technical leader ready to architect core AI systems and write production code daily.
What you’ll do:
- Architect cloud infrastructure for distributed ML pipelines using PyTorch, including cluster management and spot instance orchestration
- Design, prototype, and train large models integrating multi-omic and clinical data
- Lead MLOps: CI/CD, model deployment, experiment tracking, and reproducibility
- Collaborate with scientists and data engineers to translate biological data into predictive AI systems
- Contribute to foundational model architecture decisions and future open-source initiatives
Your profile:
- 5+ years building and deploying ML systems at scale
- Expertise in PyTorch and modern ML tooling (ML Flow, Weights)
We’re Hiring: Machine Learning Engineer employer: Generative
As a Machine Learning Researcher at our London-based AI lab, you'll be part of a dynamic and ambitious team dedicated to solving significant scientific challenges through innovative AI solutions. We foster a collaborative and inclusive work culture that values curiosity and low ego, offering flexible hybrid working arrangements and opportunities for professional growth in a supportive environment. Join us to make a tangible impact from day one, as you contribute to cutting-edge research that accelerates the discovery of commercially valuable materials.
