---
title: 'Machine Learning & Cloud Infra Engineer at SpAItial'
canonical: 'https://feeny.ai/job/machine-learning-cloud-infra-engineer-spaitial-london-p5w3sxeysvq7'
type: 'job'
last_seen: '2026-09-06'
---

# Machine Learning & Cloud Infra Engineer at SpAItial

- **Company:** SpAItial
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-04-14
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/spaitial/1cf5f91a-9f66-48a8-aaee-18a18349785d

## 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 & Cloud Infra Engineer to build and own the infrastructure that powers our World Model research and productization. You will design, implement, and operate scalable training and data systems for large diffusion-based generative models, spanning GPU clusters, storage, orchestration, and reliable model serving. This role is hands-on and systems-focused, enabling researchers and engineers to train, evaluate, and deploy world-scale models efficiently and safely.

## Responsibilities

- Own and evolve the ML + cloud infrastructure that enables training and evaluation of massive foundation models.
- Design and operate GPU clusters: Provision, scale, and maintain multi-node, multi-GPU training environments (on cloud and/or on-prem), including scheduling, quotas, and capacity planning.
- Distributed training enablement: Support high-throughput training stacks (e.g., PyTorch DDP/FSDP, NCCL) and ensure performance, stability, and reproducibility across large runs.
- Storage and data throughput: Build and optimize storage systems and networking for petabyte-scale datasets and high-bandwidth training (object storage, NVMe, shared filesystems, caching, data locality).
- Containerization and orchestration: Package and deploy workloads with Docker and Kubernetes (or comparable systems); maintain infrastructure-as-code (Terraform) and reliable release processes.
- Observability and reliability: Implement monitoring, logging, and alerting for cluster health, job performance, and cost; define SLOs and on-call/incident response practices.
- Security and access: Manage secrets, IAM, and secure network boundaries for research and production systems.
- Collaboration: Partner closely with ML researchers and engineers to unblock training, iterate on tooling, and improve developer experience.
- Production pathways: Support model evaluation and serving infrastructure where needed, and ensure smooth transitions from research to deployable systems.

Key Qualifications:

- 3+ years of professional experience in infrastructure, platform, or cloud engineering (ML infrastructure experience strongly preferred).
- Hands-on experience with GPU compute and performance debugging (CUDA/NCCL concepts, GPU utilization, networking bottlenecks, profiling).
- Strong experience operating cloud environments (AWS, GCP, or Azure), including networking, IAM, and cost management.
- Proficiency with containers and orchestration (Docker, Kubernetes) and infrastructure-as-code (Terraform).
- Strong scripting and automation skills (Python plus Bash/PowerShell).
- Familiarity with distributed training and modern ML stacks (PyTorch; DDP/FSDP or comparable).
- Experience with monitoring and observability tooling (Prometheus/Grafana, OpenTelemetry, ELK, or similar).
- Experience building CI/CD for infra and ML workflows (e.g., CircleCI, 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.

## 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 Systems & Infrastructure Engineer](https://feeny.ai/job/machine-learning-systems-infrastructure-engineer-spaitial-london-yg2ny2hqnfkr) — 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