Senior Machine Learning Engineer

Senior Machine Learning Engineer - Foundation Models for Drug Discovery

📍 London, UK (Hybrid) | 💰 Up to £145,000 + Equity

🧬 AI Biotech Startup | Full-time

About Us

I'm working with an AI-first biotech company building foundation models for biology and chemistry to radically accelerate drug discovery. By combining probabilistic modelling, large-scale biological datasets, and generative machine learning, they're creating systems capable of reasoning across molecular, genomic, and experimental data.

Backed by leading investors and research institutions, our mission is to build generalisable AI models that can predict biological behaviour, reduce wet-lab iteration cycles, and unlock novel therapeutics for previously intractable diseases.

Our team includes researchers and engineers from DeepMind, Recursion, Isomorphic Labs, etc.

The Opportunity

We’re hiring a Senior Machine Learning Engineer to work across foundation models, probabilistic inference, and computational biology.

You’ll help design and scale next-generation models trained on massive multimodal biological datasets, with applications spanning molecular generation, protein representation learning, and uncertainty-aware prediction systems.

This role is ideal for someone excited by frontier ML research but equally passionate about building robust, production-grade systems.

You’ll Be Working On

• Developing large-scale foundation models for biological and chemical data
• Building probabilistic ML systems capable of uncertainty estimation and Bayesian inference
• Training transformer architectures and latent variable models across molecular and genomic datasets
• Scaling distributed GPU training pipelines for multi-billion parameter models
• Working on generative modelling approaches for molecule and protein design
• Designing evaluation frameworks for biological plausibility, calibration, and model reliability
• Collaborating closely with computational biologists and research scientists to integrate models into discovery workflows
• Driving best practices across MLOps, experimentation infrastructure, and model deployment

What They're Looking For

Essential

• 5+ years experience building advanced machine learning systems in production
• Strong background in deep learning and modern foundation model architectures
• Experience with probabilistic modelling techniques such as Bayesian methods, Gaussian Processes, or variational inference
• Expertise in PyTorch, JAX, or similar frameworks
• Experience training large models on distributed infrastructure
• Strong software engineering and systems design capabilities

Nice to Have

• Background in computational biology, drug discovery, or bioinformatics
• Experience working with molecular, genomic, or protein datasets
• Familiarity with diffusion models, energy-based models, or probabilistic generative methods
• Publications in ML, AI for Science, or related research areas
• Experience optimising large-scale training workloads on GPU clusters

Why Join?

• Work on genuinely frontier AI research with real-world scientific impact
• Opportunity to help define the future of AI-native drug discovery
• Competitive salary + meaningful equity package
• Access to significant compute resources and cutting-edge datasets
• Collaborative environment with world-class researchers and engineers
• Flexible hybrid working and generous learning budget

Please apply directly via this job post.