Senior​/Machine Learning Scientist

Glasgow (onsite, full-time) or fully remote with regular travel to our Glasgow HQ / Chemifarm

Chemify is revolutionising chemistry. We are creating a future where the synthesis of previously unimaginable molecules, drugs, and materials is instantly accessible. By combining AI, robotics, and the world's largest continually expanding database of chemical programs, we are accelerating chemical discovery to improve quality of life and extend the reach of humanity.

Our Chemifarm facility in Glasgow operates a growing fleet of advanced robotic systems that automate synthesis, optimisation, and library generation. This gives our computational scientists something rare: a direct, high-throughput bridge from in silico design to physically synthesised molecules, closing the design–make–test loop at a pace conventional drug discovery organisations cannot match.

We are seeking a Senior / Staff Machine Learning Scientist to work across the breadth of Chemify's platform — generative models for chemistry, search and planning for retrosynthesis, computer vision for telemetry from our robotic systems, and agentic workflows that tie it all together. You will partner with computational chemists, CADD scientists, software engineers, and hardware engineers, and apply AI/ML to build the next generation of Chemify's platform.

What sets this role apart is the combination of breadth of ML problems — generative chemistry, vision, search, agents — paired with a robotic platform that turns your models into physical experiments.

If working across a wide range of hard ML problems on a real-world platform sounds like the right shape of job for you, we'd love to welcome you to our team.

• Build generative and foundation chemistry models for molecular design
• Advance retrosynthesis and synthesis‑aware ML by leveraging Chemify's reaction database and robot‑execution data
• Apply computer vision to transform robot telemetry into models that monitor process state and feedback into experimental control
• Prototype agentic workflows that orchestrate models, tools, and the platform — closing loops between proposal, execution, observation, and learning
• Product ionise models into a reproducible, API‑first toolkit; partner with Infrastructure on GPU training and HPC; maintain high standards of ML best practices, including rigorous evaluation, benchmarks, and reproducibility
• Mentor junior ML scientists, partner with the Head of Advanced Machine Learning on hiring and growth, and represent Chemify's AI/ML capability externally
• (Staff level) Set technical direction across the AI/ML stack; lead cross‑cutting initiatives spanning chemistry models, retrosynthesis, vision, and agents

You are an experienced ML scientist who is equally comfortable training models and shipping the code that other people end up building on. You care about whether your model changes a real decision — not just whether it beats a benchmark. You're at home moving across problem types, from generative models to vision to search.

• PhD or equivalent experience in Machine Learning, Computer Science, Statistics, Physics, or a related quantitative field — 5+ years (Senior) or 8+ years (Staff) of hands‑on applied ML experience, including production‑grade work
• Deep familiarity with modern deep learning stack (PyTorch or JAX), and breadth across at least two of: generative models (diffusion, autoregressive, flow‑based), graph and equivariant networks, vision (CNNs, ViTs, multimodal LLMs), search and planning (MCTS, A*), or agentic / RL systems
• Experience taking ML from prototype to production: reproducible pipelines, distributed jobs, and batch workflows on cloud (AWS / GCP / Azure) or HPC
• Strong scientific computing instincts: clean Python, careful experiment design, leakage‑aware splits, and rigorous benchmarks
• Clear communication with non‑ML scientists and engineers and a willingness to pick up new domains (you don't need to know chemistry on day one)
• (Staff level) A track record of technical leadership: mentoring, setting standards, and influencing scientific and technical direction beyond your own projects

• Pra…