Research Scientist

PhysicsX · London, United Kingdom

Research London Posted Apr 28, 2026

About us

PhysicsX 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, PhysicsX 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 levels and positions, however please only apply for the role that best aligns with your skillset and career goals.

What you will do

Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques.
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.
Collaborate with colleagues beyond the research team to translate your models into production-ready code.
Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.
Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and you to mentor.

What you bring to the table

Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
Ability to scope and effectively deliver projects.
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.
PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
1. operator learning (neural operators), or other probabilistic methods for PDEs;
2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
>2 years of experience in a data-driven role in a professional industry setting (excluding post-doc positions), with exposure to:
- 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;
- developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
- iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;
- combining theoretical reasoning with empirical intuition to guide investigation;
- formulating and running experiment pipelines to benchmark models and produce comparable results;
- writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience.
Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.

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.

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PhysicsX

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Machine Learning Software Engineer, Research

PhysicsX · London, United Kingdom

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Research London Posted Apr 27, 2026

About us

PhysicsX 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, PhysicsX 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.