5+ years of experience in engineering production distributed systems or ML infrastructure
Strong proficiency in Python, plus knowledge of Go or C++
Extensive experience with Kubernetes and familiarity with Slurm or similar schedulers
Demonstrated ability to manage large GPU workloads in modern training or serving environments
Commitment to operational reliability, including willingness to handle on-call responsibilities
Responsibilities
Build and manage GPU clusters across various environments including public cloud and owned data centers
Design and implement schedulers and automation for improved resource utilization and fault recovery
Establish fast model serving and distributed training setups
Analyze and report on compute spending to identify cost-saving opportunities
Benefits
Opportunity for real ownership and visible impact from day one
Engagement with cutting-edge technology in GPU and ML infrastructure
Collaboration with researchers on critical projects
Dynamic work environment with a focus on innovation
Full Job Description
As a senior engineer, you will write production code that decides how large fleets of accelerators are scheduled, served and repaired. You will become the engineer researchers call when a critical run has to go right, with real ownership and visible impact from day one.
What you will do:
Build and operate GPU clusters across public cloud, specialty GPU providers and owned data centers
Design schedulers and automation that increase utilization and recover from hardware failures without manual intervention
Stand up fast model serving and distributed training environments
Report on where compute spend goes and where it can be reduced
What you bring:
5+ years engineering production distributed systems or ML infrastructure
Strong Python plus Go or C++
Deep Kubernetes experience, with exposure to Slurm or similar schedulers
Proven experience running large GPU workloads on a modern training or serving stack
Willingness to carry the pager for systems that other teams depend on
