Machine Learning Software Engineer, Research

PhysicsX

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  * Shape Research group strategy and culture in a significant way, especially in domains of expertise. * Be opinionated and formulate strategy on engineering topics relevant to our Research priorities, especially on: scaled engineering, securing compute, infrastructure stack. * Define necessary profiles to execute this strategy. * Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment. * Nurture younger colleagues to grow their skillset and guide their professional development. * Own Research work-streams at a high-level to deliver outcomes. * Align priorities with problem stakeholders, internal and external. * Set the technical direction for the stream and apply judgement and taste to drive progress. * Plan roadmaps with clear milestones for key decisions and outcomes. * Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap. * Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment. * The below activities in particular. * 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. * 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. *  4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in most of the below: * * scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus); * employing distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton); * employing 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; * building or using C/C++ for computer vision, geometry processing, or scientific computing; * following and promoting software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps); * container-izing and orchestrating compute tasks (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.

PhysicsX

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, HOWEVER PLEASE ONLY APPLY FOR THE ROLE THAT BEST ALIGNS WITH YOUR SKILLSET AND CAREER GOALS. THE ROLE The Principal ML Infrastructure Engineer will extend and operate the infrastructure that powers our research model training, fine-tuning, and serving pipelines. You will be embedded within our Research function, partnering directly with ML engineers and research scientists to ensure they can train Large Physics Models efficiently and reliably at scale. TEAM CONTEXT In this role, you will be vertically embedded in Research, working daily with: * Research Scientists who determine the model architectures and methods * ML Engineers who implement and develop the models * Simulation Data Engineers who are accountable for upstream data pipelines You will have end-to-end responsibilities over the research infrastructure, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably. Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company. WHAT YOU WILL DO TRAINING INFRASTRUCTURE * Design and operate distributed training infrastructure for neural operator architectures (Transolver, Point Cloud Transformer, etc.) on our large NVIDIA DGX B200 platform. * Optimize training pipelines for throughput, fault tolerance, and cost efficiency, including checkpointing strategies, gradient accumulation, and multi-node synchronization. * Build and maintain experiment tracking and observability systems that give researchers clear visibility into training runs, hyperparameter sweeps, and model performance. DATA I/O AND PERFORMANCE * Solve data loading bottlenecks for large-scale mesh datasets. * Optimize data pipelines for efficient I/O from cloud storage, including prefetching, caching, and format optimization. * Work with heterogeneous data sources of varying formats and resolutions. MODEL SERVING AND DEPLOYMENT * Build serving infrastructure for pre-trained LPMs, supporting both zero-shot inference and uncertainty quantification (Monte Carlo Dropout). * Design and implement model packaging pipelines for customer deployment. Models must run reliably in customer environments with fine-tuning capabilities. * Ensure reproducibility: any model checkpoint should be deployable with consistent behaviour. PLATFORM AND TOOLING * Improve developer experience for the Research team with fast iteration cycles, reliable CI/CD, clear debugging tools. * Collaborate with the broader Infrastructure team on shared patterns and standards. WHAT YOU BRING TO THE TABLE * Ability to scope and effectively deliver projects, prioritising activity as needed. * Problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. * Excellent collaboration and communication skills, especially in a research setting. You can translate "the model isn't converging" into infrastructure hypotheses and solutions, and can bridge technical abstractions with implementations. * 5+ years of experience building and operating ML infrastructure at scale: * Deep expertise in distributed training: you've debugged NCCL hangs, optimized collective communication, and know when to use FSDP vs. DDP vs. pipeline parallelism * Strong systems fundamentals: Linux, networking (including domain specific NVLink and InfiniBand), storage I/O, profiling and performance optimization * Production experience with Kubernetes and SLURM for job orchestration on GPU clusters * Proficiency in Python and ML frameworks (PyTorch strongly preferred) * Experience with cloud GPU infrastructure; ideally CoreWeave or similar GPU/HPC-focused clouds IDEALLY * Experience with geometric deep learning or neural operators, ****architectures that operate on meshes, point clouds, or graphs * Background in HPC for simulation engineering, familiarity with how CFD/FEA workflows generate and consume data * Experience building model serving infrastructure with latency and throughput requirements * Familiarity with experiment tracking tools (Weights & Biases, MLflow) and observability stacks (Prometheus, Grafana) * Experience packaging models for deployment into customer environments (containers, model registries, versioning)   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.

Graphcore

ABOUT GRAPHCORE At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. JOB SUMMARY As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware.   THE TEAM Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world. We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations. Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you! RESPONSIBILITIES AND DUTIES * Generate AI/ML ideas, design experiments, implement them & evaluate results. * Prepare, submit & present your work to AI conferences and workshops. * Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports. * Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.   ABOUT YOU: Essential: *  Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry). * Python programming in a modern deep learning framework, e.g. PyTorch or JAX. * Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. * Capable of designing, executing and reporting from ML experiments. * Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton. * Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. * Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution Desirable: * Mathematics skills to support the above: calculus, probability theory and linear algebra. * Experience submitting papers to international scientific conferences or workshops.   BENEFITS In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.   Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications