2 months ago
San Jose, CA, USASenior
Responsibilities
- Design and develop Fleet Manager distributed control-plane services, APIs, schedulers, inference gateways, and command-line tools.
- Build orchestration, scheduling, admission control, reconciliation, lifecycle management, and recovery for GPU workloads.
- Integrate the platform with Kubernetes, Kueue, JobSet, container runtimes, storage systems, observability platforms, Slurm, Spur, and future execution environments.
- Develop GPU health, diagnostics, quarantine, remediation, secure multi-tenant infrastructure, and reliable AI inference capabilities.
- Define stable APIs, data models, compatibility contracts, and operational procedures while diagnosing failures across services, clusters, networking, storage, runtimes, drivers, and hardware.
- Develop automated tests and provide technical leadership through architecture, design reviews, code reviews, mentoring, and cross-functional collaboration.
Requirements
- Strong systems-software development experience in Rust, C++, Go, or a comparable language; production Rust experience is highly desirable.
- Experience designing and operating distributed systems, control planes, schedulers, or cloud infrastructure.
- Strong understanding of concurrency, asynchronous programming, state machines, and failure recovery.
- Experience building reliable services with REST, streaming, WebSocket, or gRPC APIs.
- Experience with Kubernetes internals, controllers, operators, scheduling, resource management, or custom resources.
- Experience with PostgreSQL-backed services, schema evolution, transactions, leader election, and optimistic concurrency.
- Experience developing command-line tools and stable user-focused APIs.
- Understanding of container security, multi-tenant isolation, authentication, authorization, and secrets management.
- Experience with production observability, metrics, structured logging, tracing, alerting, incident diagnosis, source control, continuous integration, automated testing, profiling, and debugging tools.
- Demonstrated ability to lead technically challenging projects and collaborate across organizational boundaries, with effective written and verbal communication skills.
- Beneficial experience includes AMD GPU architecture, ROCm, HIP, amd-smi, RCCL, GPU device plugins, distributed AI training, multi-node collective communication, vLLM, SGLang, PyTorch, GPU topology, capacity management, Kubernetes networking and storage, inference APIs, shared storage, and GPU infrastructure.
Benefits
- AMD benefits are referenced through an external “Benefits at a glance” resource; specific benefits are not provided in the posting.
Tech Stack
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.