Machine Learning & Cloud Infra Engineer

Role 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.