About the Job
Foundational ML Research Engineer
Stealth AI Company | London | Strong base + meaningful equity
We’re working with a well-funded stealth AI company building at the intersection of AI, science, and real-world experimentation.
They’re early, highly ambitious, and backed by top-tier investors. The team is small, technical, high-calibre, and moving quickly. They’re now looking for a foundational ML Research Engineer to help build the systems behind an AI-driven science-based discovery engine.
This is not a narrow “train a model and hand it over” role. You’ll work across research and engineering, turning new ideas in modelling, reasoning, optimisation, and experiment automation into robust, scalable systems that can drive real-world scientific discovery.
The work is high-impact because the models directly connect to physical experimentation. Every improvement in training speed, model performance, evaluation quality, data pipeline design, or experiment feedback loop helps the team learn faster and discover more innovations.
What You’ll Work On
- Translate cutting-edge ML research and novel architectures into performant, scalable implementations.
- Build large-scale training pipelines and inference systems across GPU environments.Profile and optimise model code across compute, memory, data loading, and distributed training bottlenecks.
- Develop evaluation frameworks that connect model predictions with real-world lab outcomes.
- Build experiment-tracking and reproducibility tooling to improve research iteration cycles.
- Curate and architect pipelines for complex multimodal scientific data, including simulations, structured lab outputs, and unstructured text.
- Work closely with AI researchers, materials scientists, and software engineers to take research ideas from paper or prototype into tested, deployable systems.
- Help build models that are not just theoretically interesting, but practically useful inside a closed-loop experimentation environment.
What We’re Looking For
- Strong machine learning, research engineering, or ML systems background.
- Deep understanding of modern ML architectures such as Transformers, GNNs, diffusion models, or related approaches.
- Strong hands-on experience with PyTorch, JAX, or similar deep learning frameworks.
- Excellent Python engineering skills and comfort writing clean, tested, production-quality MLcode.
- Experience building or scaling ML systems, including training infrastructure, deployment workflows, and experiment pipelines.
- Comfortable taking research ideas, papers, or early prototypes and turning them into working systems.
- Strong bias for ownership, pragmatism, and technical depth in an early-stage environment.
- Curious, collaborative, self-driven, and excited by hard scientific and engineering problems.
Nice to Have
- Experience with large-scale or distributed training across multi-GPU or multi-node environments.
- Experience optimising ML workloads on GPU clusters.
- Background in scientific computing, simulation, research infrastructure, materials science, biotech, robotics, physics, chemistry, or other deep tech environments.
- Familiarity with scientific data formats, reproducibility practices, and experiment tracking.
- Experience with GCP, Kubernetes, Docker, distributed systems, or ML infrastructure.
- Exposure to closed-loop systems, autonomous experimentation, lab automation, or physical-world AI.
Why This is Interesting
- Stealth AI company with serious backing.
- Foundational role with huge technical ownership.
- Work at the intersection of ML, science, and real-world experimentation.
- Build systems that directly support material discovery.
- Small, high-calibre team across AI, engineering, and science.
- Very little legacy — you’ll help shape how the research and ML systems are built.
- Strong compensation and meaningful equity.
- London HQ, with flexibility for strong people across Europe / similar time zones.
If you’re an ML Research Engineer, Research Engineer, Applied ML Engineer, ML Systems Engineer, or AI Engineer who wants to turn cutting-edge research into real-world scientific impact, I’d love to chat.
Reach out at danny@salientgroup.com.au
