Machine Learning Software Engineer, Research

• Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems.
• Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain.
• Transform prototype model implementations to robust and optimised implementations.
• Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi-node/multi-GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on-premise services.
• Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimise model training to large data and multi-GPU cloud compute.
• Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success.
• Own Research work-streams at different levels, depending on seniority.
• Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
• Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.
• Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor.

• Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.
• Ability to work autonomously and scope and effectively deliver projects across a variety of domains.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following:
  
  • Scientific computing;
  • High-performance computing (CPU / GPU clusters);
  • Parallelised / distributed training for large / foundation models.
• Ideally >2 years of experience in a data-driven role in a professional setting, with exposure to:
  • scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);
  • distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);
  • cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);
  • building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
  • C/C++ for computer vision, geometry processing, or scientific computing;
  • software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
  • container-ization and orchestration (Docker, Kubernetes, Slurm);
  • writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…