PhysicsX Ltd
Senior Machine Learning Infrastructure Engineer London, United Kingdom 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 Senior 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) What we offer 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. 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.
Senior Machine Learning Infrastructure Engineer London, United Kingdom 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 Senior 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) What we offer 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. 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 Ltd
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 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. Contribute towards Research group strategy and culture. Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives. 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. The below activities in particular. 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. 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. What you bring to the table Ability to scope and effectively deliver projects. Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. 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: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point cloud or mesh structured data; generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non parametric, scaling to large datasets, etc.). Ideally, >4 years of experience in a data driven role in a professional industry setting, where you have been instrumental in: building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch / CUDA, ideally with exposure to JAX, NumPy / SciPy), 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. 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. 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 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 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. Contribute towards Research group strategy and culture. Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives. 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. The below activities in particular. 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. 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. What you bring to the table Ability to scope and effectively deliver projects. Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. 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: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point cloud or mesh structured data; generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non parametric, scaling to large datasets, etc.). Ideally, >4 years of experience in a data driven role in a professional industry setting, where you have been instrumental in: building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch / CUDA, ideally with exposure to JAX, NumPy / SciPy), 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. 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. 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.