--- title: 'Machine Learning Systems & Infrastructure Engineer at SpAItial' canonical: 'https://feeny.ai/job/machine-learning-systems-infrastructure-engineer-spaitial-london-yg2ny2hqnfkr' type: 'job' last_seen: '2026-09-13' --- # Machine Learning Systems & Infrastructure Engineer at SpAItial - **Company:** SpAItial - **Location:** London, United Kingdom - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-05-06 - **Last confirmed live:** 2026-09-13 - **Apply:** https://jobs.ashbyhq.com/spaitial/3ba04d10-3097-44f3-9058-003a747944d8 ## Job description 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. ## About SpAItial ## Company Overview - **One-liner**: SpAItial builds physically-grounded world models — AI that generates and reasons about the appearance and physics of real and imagined 3D environments. - **Entity Type**: Private (Seed stage) - **Headquarters**: London, United Kingdom (also has offices in Germany, United States, Luxembourg) - **Founded**: 2024 (LinkedIn) / 2025-01-01 (company schema) — likely incorporated in late 2024, publicly launched May 2025 - **Founders**: Matthias Niessner (CEO), Luke Rogers (COO), Ricardo Martin‑Brualla, David Novotný ## Core Business - **Primary industry/industries**: Spatial AI, 3D generative AI, foundation models, computer graphics - **Target customers**: Developers, game studios, film/VFX, VR/AR platforms, robotics companies, industrial simulation (B2B); also provides a consumer app for world generation (B2C) - **Mission or purpose**: “Bridging the virtual and physical – building physics‑consistent, spatio‑temporally grounded AI that understands the 3D world like humans do.” ## Products & Services - **[Echo (Model Family)](https://spaitial.ai/)**: A series of Spatial Foundation Models (Echo‑1, Echo‑2, Echo HQ) that generate persistent 3D Gaussian Splat worlds from a single image, text prompt, or 360° panorama. Outputs are real‑time explorable and physically plausible. - **[SpAItial API](https://spaitial.ai/)**: Programmatic access to Echo for agents, tools, simulations, and creative pipelines. Supports async generation, webhooks, and multiple output formats (SPZ, SOG, PLY, collision meshes). - **[SpAItial App](https://spaitial.ai/)**: A web and desktop application for creating, editing, and sharing worlds generated by Echo. Includes image‑to‑world, text‑to‑world, sculpting, painting, and export tools. - **Startup Program**: Credits, priority API access, early model access, and direct feedback loops for startups building with spatial AI. ## Market Standing - **Valuation/Market Cap**: Not disclosed (private company) - **Key Metric**: **Total Funding** — USD 13,000,000 (Seed round, closed June 27, 2025) - **Notable Investors/Partners**: - Lead: Earlybird Venture Capital - Participants: Speedinvest - Notable angels: Robin Rombach (Black Forest Labs), Victor Riparbelli & Steffen Tjerrild (Synthesia), Christian Stiebner, Edward Grefenstette, Jon Barron, Rahul Garg, Sergej Epp (Sysdig/Palo Alto), Elias Schneider (Codesphere), Paul Whitehead (Zoopla), and others. - **Growth Signals**: - Headcount: 20 employees (+2000% YoY from 1 employee the year prior) - Active job postings: 11 (monthly growth +22.2%, quarterly +175%) - Strong hiring across research, engineering, and ML infrastructure - Launched Echo‑2 in May 2026, Echo HQ in June 2026 - Opened early access and startup program ## Competitive Advantages - **Spatial Foundation Models (SFMs)**: Unlike LLMs, image, or video models, SFMs operate natively in 3D physical space, capturing geometry, materiality, and physics — enabling true spatial reasoning and physically consistent generation. - **World‑class founding team**: Deep expertise from academia (TU Munich), big tech (Google, Meta), and scaled startups (Synthesia, Cazoo). Includes pioneers in generative 3D (GRAF, VoxGRAF, X‑Fields). - **Real‑time, persistent 3D worlds**: Outputs are not just images or videos but fully explorable Gaussian Splat environments that can be edited, exported, and shared. - **Strong investor backing**: $13M seed from top European VCs and influential angel investors validates the technology and vision. ## Strategic Focus - **Model scaling**: Improving core model capabilities (Echo‑2, Echo HQ) for richer geometry, sharper detail, and stability. - **Developer ecosystem**: Building out the API, documentation, and startup program to attract developers and partners. - **Industry partnerships**: Piloting technology across gaming, film, CAD, VR/AR, and robotics — collaborating with leading organizations. - **Talent acquisition**: Growing the team aggressively across research, engineering, and infrastructure. ## Why Work Here - **Cutting‑edge research**: Work on frontier AI problems at the intersection of computer vision, graphics, and 3D generative models. Publishable research encouraged. - **Strong team**: Colleagues from Meta, Google, TU Munich, Synthesia, and top labs. Low ego, high output. - **Early stage impact**: Join at 20 people — your work directly shapes the product, culture, and direction of the company. - **Global, hybrid‑friendly**: Headquarters in London with presence in Munich (Germany), New York (US), and Luxembourg. Mix of in‑person and remote collaboration. - **Open roles** (as of mid‑2026): Research Scientist (3D Diffusion, World Models), Research Engineer (Graphics, World Models), ML Infrastructure Engineer — 11 active postings. - **Perks**: Not publicly detailed, but seed‑stage startup typically offers equity, flexible time off, and direct access to founders. - **Culture**: Described as “research meets real‑world application” — a blend of academic rigor and product‑focused execution. ## Sources 1. [spaitial.ai](https://spaitial.ai/) – Company homepage, product overview, API docs, blog 2. [spaitial.ai/about](https://spaitial.ai/about) – About page, mission, team, funding announcement 3. [spaitial.ai/careers](https://spaitial.ai/careers) – Careers page, open roles 4. [spaitial.ai/blog/announcing-spaitial](https://spaitial.ai/blog/announcing-spaitial) – Launch blog post with detailed team, funding, and vision 5. [LinkedIn – SpAItial AI](https://uk.linkedin.com/company/spaitial-ai) – Employee count, funding, headcount growth, open roles, talent sources ## Other roles at SpAItial - [Technical Artist](https://feeny.ai/job/technical-artist-spaitial-london-kdgkjp9c2nx6) — London, United Kingdom - [Business Operations Manager](https://feeny.ai/job/business-operations-manager-spaitial-london-fwsemcnhjvws) — London, United Kingdom - [Research Scientist - Robot Learning (VLA / WAM)](https://feeny.ai/job/research-scientist-robot-learning-vla-wam-spaitial-london-97ghdtvn8cmv) — London, United Kingdom - [Research Scientist - 3D Reconstruction (SfM & SLAM)](https://feeny.ai/job/research-scientist-3d-reconstruction-sfm-slam-spaitial-london-dgx907pgrv08) — London, United Kingdom - [Machine Learning & Cloud Infra Engineer](https://feeny.ai/job/machine-learning-cloud-infra-engineer-spaitial-london-p5w3sxeysvq7) — London, United Kingdom - [Research Scientist - 3D Diffusion](https://feeny.ai/job/research-scientist-3d-diffusion-spaitial-london-f21y5vjxxmzm) — London, United Kingdom - [Research Engineer - 3D World Models](https://feeny.ai/job/research-engineer-3d-world-models-spaitial-london-68tajf2mpe9t) — London, United Kingdom - [Research Engineer - Graphics](https://feeny.ai/job/research-engineer-graphics-spaitial-london-scfpn9d65rd2) — London, United Kingdom - [Research Scientist - World Models](https://feeny.ai/job/research-scientist-world-models-spaitial-london-txvdt70j5zfj) — London, United Kingdom