Machine Learning Software Engineer, Research

PhysicsX Ltd; City of Westminster; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

A leading physics and engineering solutions company in the United Kingdom is looking for a Machine Learning Scientist to design and build optimised models for real-world physics and engineering challenges. This role involves collaboration with research scientists, implementing distributed training architectures, and the development of scalable machine learning solutions. The ideal candidate should have a strong background in machine learning, scientific computing, and Python, with opportunities for personal development and a hybrid working setup.

Benefits

Equity options

10% employer pension contribution

Free office lunches

Flexible working

Hybrid setup

Enhanced parental leave

Private healthcare

Personal development

Work from anywhere

Qualifications

• Strong understanding of machine learning and data science.
• Experience in building and optimizing machine learning models.
• Ability to implement distributed training architectures.

Responsibilities

• Work with research scientists to design and build scalable machine learning models.
• Implement distributed training architectures for multi-node/multi-GPU training.
• Discuss results and their implications with colleagues and customers.

Skills

Machine learning

Problem-solving

Collaboration

Communication

Scientific computing

Python

Education

MSc or PhD in computer science, machine learning, or related field

Tools

PyTorch

NumPy

Docker

Kubernetes

AWS

Azure

Job description

• 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 >1 years of experience in a data‑driven role, 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‑isation and orchestration (Docker, Kubernetes, Slurm)
  • Writing pipelines and experiment environments, including running experiments in pipelines in a systematic way
• Equity options - share in our success and growth.
• 10% employer pension contribution - invest in your future.
• Free office lunches - great food to fuel your workdays.
• Flexible working - balance your work and life in a way that works for you.
• Hybrid setup - enjoy our new Shoreditch office while keeping remote flexibility.
• Enhanced parental leave - support for life's biggest milestones.
• Private healthcare - comprehensive coverage
• Personal development - access learning and training to help you grow.
• Work from anywhere - extend your remote setup to enjoy the sun or reconnect with loved ones.