Job
- Level
- Experienced
- Job Field
- Software, Data
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Munich
- Working Model
- Onsite
Job Summary
In this role, you will build robust ML systems and infrastructure to transform real-world data into trained 3D world models and create automated production endpoints while closely collaborating with the research team.
Job Technologies
Your role in the team
- 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.
- 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.
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Our expectations of you
Qualifications
- 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.
- Praktische GPU-Compute- und Performance-Debugging (CUDA/NCCL, GPU-Auslastung, Netzwerkengpässe, 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.
- Vertrautheit mit ML-Workflow-Orchestrierung und Experiment-Tracking (z. B. Kubeflow Pipelines, MLflow).
Experience
- 3+ years writing production-quality Python in a large, multi-author codebase, with strong SWE fundamentals (ML systems experience strongly preferred).
- Experience with monitoring and observability tooling (e.g., Prometheus/Grafana, OpenTelemetry) and CI/CD for infra and ML workflows (e.g., GitHub Actions).
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What we offer
- SpAItial is 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.
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Topics that you deal with on the job
Job Locations
This is your employer
SpAItial
SpAItial is an innovative AI startup based in Munich that focuses on the development of Spatial Foundation Models. These models allow for the generation of physically accurate 3D environments from various inputs and are used in fields such as gaming, robotics, and AR/VR. Founded by Prof. Matthias Niessner and his team, the company combines research from Munich and London.
Description
- Company Type
- Startup
- Working Model
- Onsite
- Industry
- Internet, IT, Telecommunication
Machine Learning Systems & Infrastructure Engineer
- Location
- Munich
- Working Model
- Onsite
- Diversity
- Open for all genders
- English Only
- English only required
