Full-Time Senior Cloud Infrastructure Engineer

Waabi, founded by AI pioneer and visionary Raquel Urtasun, is an AI company building the next generation of self-driving technology.

Waabi is looking for a Senior/Staff Cloud Infrastructure Engineer to help improve ML infrastructure and support company engineering and research efforts.

You will:

• Work alongside engineers and researchers using an AI-first approach to enable safe self-driving at scale.
• Collaborate with cross-functional teams to understand cloud usage needs and pain points.
• Propose cloud strategies for compute and data usage in training and simulation workloads.
• Design and implement scalable and resilient cloud infrastructure for long-term reliability and adaptability.
• Devise and promote best practices for cloud usage in training and simulation environments, overseeing cloud strategies company-wide.

Qualifications:

• BS, MS/PhD in Computer Science or similar technical field, or equivalent practical experience.
• 5+ years of relevant industry experience.
• Experience in reading and developing production-quality software.
• Deep understanding of cloud compute and data storage for distributed training and inference workloads.
• Familiarity with Python, GO, Rust, or C++ ecosystems.
• Experience with public cloud platforms (AWS preferred).
• Experience with infrastructure as code systems (Terraform preferred).
• Experience in job scheduling and resource allocation.
• Experience with containers and container orchestration (Docker, ECS, Kubernetes).
• Experience and comfort working with Linux systems.
• Experience building platform services enabling other teams.
• Open-minded and collaborative team player.
• Passionate about self-driving, solving hard problems, and creating innovative solutions.
• Experience working in an Agile/Scrum environment.

Bonus Qualifications:

• Experience with on-premise servers, network equipment, and scale-out storage systems.
• Experience with CI/CD pipelines and release management.
• Experience with common ML tools, workflows, and frameworks (Kubeflow, MLFlow).
• Understanding of system performance tuning at software, hardware, and network levels.

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