Member of Technical Staff — Infrastructure
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• Build and operate GPU clusters, job scheduling, networking, and storage for distributed ML workloads.
• Develop the execution platform for large numbers of concurrent environments, including sandbox isolation, resource limits, retries, and state recovery.
• Build reliable data and artifact pipelines for datasets, trajectories, model weights, and checkpoints.
• Own platform observability and recovery; improve capacity allocation, startup latency, and reliability through measurable changes.
• Give researchers reproducible environments and simple tools to launch, inspect, and debug experiments. Relevant technologies
• Compute and orchestration: Linux, Docker, Kubernetes, Slurm, and Terraform.
• Execution and storage: Python or Go, Ray, S3-compatible object storage, and PostgreSQL.
• Observability: Prometheus, Grafana, OpenTelemetry, and NVIDIA DCGM; familiarity with GPU networking and NCCL is mandatory. About you You have strong systems fundamentals and can reason carefully about concurrency, resource contention, and failure recovery. You enjoy making complex infrastructure understandable and dependable for the people using it. We welcome infrastructure specialists and exceptionally fast-learning generalists. Prior ML infrastructure experience is valuable; evidence of building and operating reliable systems matters.
What we offer
• Competitive compensation and equity.
• A fast-paced environment combining frontier research with impactful real-world applications.
• Visa sponsorship and relocation support for candidates joining us in Paris or London.
• A flexible hybrid setup, with a preference for working together in person.
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