About [Employer hidden — sign up to reveal]

[Employer hidden — sign up to reveal] is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries.

What you will do

• Shape Research group strategy and culture, especially in domains of expertise.
• Be opinionated on engineering topics relevant to Research priorities: scaled engineering, securing compute, infrastructure stack.
• Define necessary profiles to execute strategy and promote effective working patterns.
• Nurture younger colleagues and guide their professional development.
• Own Research work-streams at a high-level to deliver outcomes, align priorities with stakeholders, set technical direction, plan roadmaps, organize junior team members, and communicate outcomes.
• Work closely with research scientists and simulation engineers to build and deliver models for real-world physics and engineering problems.
• Design, build and optimise machine learning models with a focus on scalability and efficiency.
• Transform prototype implementations to robust and optimised implementations.
• Implement distributed training architectures (data parallelism, parameter server) for multi-node/multi-GPU training using cloud (AWS, Azure, GCP) and on-premise services.
• Work with research scientists to design, build and scale foundation models for science and engineering.
• Identify the best libraries, frameworks and tools for modelling efforts.
• Discuss results and implications with colleagues and customers.
• Work at the intersection of data science and software engineering to translate research into reusable libraries, tooling and products.
• Foster a nurturing environment for less experienced colleagues.

What you bring to the table

• Enthusiasm for developing machine learning solutions (deep learning, probabilistic methods) and supporting software for science and engineering.
• Ability to work autonomously and scope and deliver projects across various domains.
• Strong problem-solving skills and ability to analyse issues and recommend solutions quickly.
• Excellent collaboration and communication skills with teams and customers.
• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or related field with experience in scientific computing, HPC, parallelised/distributed training for large/foundation models.
• 4+ years of professional industry experience in scaling and optimising ML models, employing distributed computing frameworks (Spark, Dask, MPI, OpenMP, CUDA, Triton), cloud computing (AWS, Azure, GCP), building ML models in Python (NumPy, SciPy, Pandas, PyTorch, JAX), building or using C/C++ for computer vision or scientific computing, following software engineering best practices (versioning, testing, CI/CD, API design, MLOps), containerising and orchestrating compute tasks (Docker, Kubernetes, Slurm), and writing pipelines and experiment environments.

What we offer

[Employer hidden — sign up to reveal] offers equity options, 10% employer pension contribution, free office lunches, enhanced parental leave, YellowNest nursery scheme, 25 days annual leave plus public holidays, private medical insurance, Wellhub subscription, eye tests, personal development support, Employee Assistance Programme, Bike2Work scheme, season ticket loan, and Octopus EV salary sacrifice. We value diversity and are committed to equal employment opportunity.