ML Platform Engineer

Role and Responsibilities:

• Design and harden the foundation for training and serving stacks, focusing on storage, indexing, streaming, and failure handling across services and regions.
• Ensure systems are observable, debuggable, and operable in production through close collaboration with software engineering, data infra, and SRE partners.

Qualifications and Experience:

• Deep understanding of Linux and a systems-oriented mindset, with experience in high-performance software such as RTB or HFT.
• Software engineering experience combined with reliability expertise, including CI/CD and strong observability instincts.
• Demonstrated ability to use AI to improve speed and quality in day-to-day workflows, with a strong track record of critical evaluation and verification of AI-assisted work.

Additional Details:

• The role involves topics like IO scheduling, lock-free data structures, kernel tuning, and robust SLIs/SLOs, applying expertise to high-leverage infrastructure at scale.
• Nice-to-haves include reverse-engineering experience, Terraform, EKS, Python, Scala, Zig, adtech or CTV experience, and experience in hard real-time low-latency environments.