Armstrong Talent Partners
Machine Learning Engineer
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Job Title
I am seeking a Machine Learning Scientist to work across the breadth of my clients platform generative models for chemistry, computer vision for telemetry from 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 the platform.
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.
Key Responsibilities
- Build generative and foundation chemistry models for molecular design.
- Advance retrosynthesis and synthesis-aware ML by leveraging the 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.
- Productionise 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 their AI/ML capability externally.
- Set technical direction across the AI/ML stack; lead cross-cutting initiatives spanning chemistry models, retrosynthesis, vision, and agents.
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About You
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.
We expect you to bring:
- PhD or equivalent experience in Machine Learning, Computer Science, Statistics, Physics, or a related quantitative field, 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).
- A track record of technical leadership: mentoring, setting standards, and influencing scientific and technical direction beyond your own projects.
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Beneficial Skills
- Practical experience with active learning, Bayesian optimisation, conformal prediction, or uncertainty quantification in iterative real-world loops.
- Familiarity with retrosynthesis ML, computer-aided synthesis planning (CASP), or reaction-condition / yield prediction.
- Working knowledge of how ML fits into a drug-discovery or materials-design workflow, plus familiarity with cheminformatics tooling (e.g. RDKit, OpenEye) or willingness to pick these up.
- MLOps fluency: experiment tracking, data versioning, model serving, and observability of deployed models.
- A visible track record in the field peer-reviewed publications, open-source contributions, or public projects that demonstrate your judgement on real ML problems.
This is a brilliant career opportunity. Apply now for an immediate interview.
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