Machine Learning & Cloud Infra Engineer

SpAItial

SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and simulation. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments. We’re looking for bold, innovative individuals driven by a passion for tackling hard problems in generative 3D AI. You should thrive in an environment where creativity meets technical challenge, take pride in craft, and collaborate closely with a small team building frontier systems. We are seeking a Machine Learning Systems & Infrastructure Engineer to build and own the systems that turn raw real-world data into trained world models and reliable production endpoints. You will design, implement, and operate scalable training stacks, data ingestion pipelines, experiment orchestration, and model serving for large diffusion-based generative models. The role is hands-on and code-heavy — you will work inside the same monorepo as the research team, mostly in Python, and should be as comfortable refactoring a trainer class or a dataset loader as you are writing Terraform. RESPONSIBILITIES * Own and evolve the ML systems that enable training, evaluation, and serving of large foundation models — trainer, dataset loaders, checkpointing, and experiment orchestration code. * Distributed training enablement: Improve high-throughput training stacks (e.g., PyTorch DDP/FSDP, NCCL) for performance, stability, and reproducibility, including preemption-safe and sharded checkpointing. * Data systems and pipelines: Build end-to-end Python pipelines that turn third-party capture sources into clean, versioned training datasets — including scraping (e.g., Playwright) and preprocessing — and optimize the underlying storage at petabyte scale (object storage, fuse mounts, caching layers, shared filesystems, and relational / analytical / embedded metadata stores). * ML workflow orchestration and serving: Operate the systems researchers use to launch experiments, data jobs, and production endpoints — workflow engines (e.g., Kubeflow Pipelines, Airflow), GPU schedulers (e.g., Volcano, Slurm), experiment trackers (e.g., MLflow, Weights & Biases), and managed-inference platforms (e.g., Modal, Triton) — and maintain a launcher SDK for one-command runs. * Containerization and packaging: Ship workloads with Docker and Kubernetes; maintain IaC (Terraform) for the surfaces you own and CI/CD pipelines, including self-hosted GPU runners. * Observability and reliability: Monitoring, logging, and alerting for job performance, data-pipeline health, and cost (e.g., Prometheus/Grafana, OpenTelemetry); define SLOs and incident response for the systems you own. * Security and access: Manage secrets, IAM, and network boundaries (e.g., Tailscale, cloud VPC) for the systems you own. * Collaboration: Partner with ML researchers, engineers, and the platform team to unblock training and data work and improve developer experience. KEY QUALIFICATIONS * 3+ years writing production-quality Python in a large, multi-author codebase, with strong SWE fundamentals (ML systems experience strongly preferred). * Hands-on with modern ML training stacks (PyTorch; DDP/FSDP or comparable); have personally debugged distributed jobs across many GPUs and nodes. * Have shipped non-trivial end-to-end data pipelines at scale — ingestion, transformation, validation, versioning, republish — ideally including real-world sources with rate limits, auth, or undocumented APIs. * Hands-on GPU compute and performance debugging (CUDA/NCCL, GPU utilization, networking bottlenecks, profiling). * Working knowledge of cloud environments (AWS, GCP, or Azure), including object storage, IAM, and cost awareness. * Proficient with containers (Docker, Kubernetes) and comfortable reading and writing IaC (Terraform) for the surfaces you ship. * Strong working knowledge of how to store and query large datasets at scale: SQL fundamentals; relational (e.g., Postgres), analytical (e.g., BigQuery, Snowflake), and embedded (e.g., SQLite) stores; and object storage with caching layers. Familiarity with ML workflow orchestration and experiment tracking (e.g., Kubeflow Pipelines, MLflow). * Experience with monitoring and observability tooling (e.g., Prometheus/Grafana, OpenTelemetry) and CI/CD for infra and ML workflows (e.g., GitHub Actions). At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process. At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.

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  * 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.

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.