Machine Learning Software Engineer, Research

United States Digital Space LLC; Greater London; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Equity options

Private medical insurance

25 days annual leave

Pension contribution

Job summary

United States Digital Space LLC is hiring in London for roles spanning ML research and software engineering. You will work with scientists to build scalable, high-performance models addressing real-world physics problems, design robust implementations, and explore distributed training across cloud and on-premise resources.

You will collaborate with a cross-disciplinary team, mentor junior colleagues, and help translate research into practical libraries and products while embracing a hybrid work

Qualifications

• MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, or similar field.
• >2 years of experience in a data-driven role with professional setting.
• Experience in scaling and optimizing ML models, training and serving foundation models at scale.
• Experience with distributed computing frameworks (e.g., Spark, Dask) and HPC frameworks (MPI, OpenMP, CUDA, Triton).
• Solid software engineering fundamentals (versioning, testing, CI/CD, API design, MLOps).
• Proficiency in Python with common libraries and deep learning tools (NumPy, SciPy, Pandas, PyTorch, JAX).
• C/C++ for computer vision, geometry processing, or scientific computing.
• Experience with containerization and orchestration (Docker, Kubernetes, Slurm).

Responsibilities

• Work closely with research scientists and simulation engineers to build models addressing real-world physics and engineering problems.
• Design, build and optimise machine learning models with focus on scalability and efficiency in our application domain.
• Transform prototype model implementations into robust, optimised implementations.
• Implement distributed training architectures for multi-node/multi-GPU training and explore federated learning in cloud and on-premise environments.
• Collaborate with researchers to design and scale foundation models for science and engineering; scale model training to large data and multi-GPU cloud compute.
• Identify best libraries, frameworks and tools for modelling efforts to ensure success.
• Own research workstreams at different levels based on seniority.
• Discuss results and implications with colleagues and customers to address real-world problems.
• Bridge data science and software engineering to create reusable libraries, tooling and products.
• Mentor colleagues with less ML/engineering experience and foster growth.

Skills

ML solutions

Autonomy

Problem solving

Collaboration

Communication

Python

C/C++

Education

MSc/PhD in CS/ML/Math/Physics

Tools

Docker

Kubernetes

Slurm

Spark

CUDA/OpenMP

Job description

About us

the company 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. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, the company unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

Note:We are currently recruiting for multiple positions across different levels, however please only apply for the role that best aligns with your skillset and career goals.

What you will do

• 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.

What you bring to the table

• 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.

What we offer

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…

We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.

We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.