ML Platform Engineer

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

• Own production inference: Take models from handoff to production-grade serving, including release engineering, capacity planning, cost optimization, and incident response.
• Tune inference performance: reduce end-to-end latency and increase throughput across real production traffic patterns.
• Optimize runtimes and servers: Scale inference across heterogeneous GPU fleets; optimize stacks such as vLLM, Triton, and related components (e.g., schedulers, KV cache, batching, memory).
• Benchmark and measure: Build benchmarking suites, metrics, and tooling to quantify latency, throughput, GPU utilization, memory, and cost.
• Reliability and observability: Improve monitoring, tracing, and alerting; participate in incident response and postmortems to harden systems.
• Apply and ship new optimizations: Evaluate research and implement pragmatic inference optimizations (e.g., quantization, paging, kernel/runtimes improvements).
• Partner cross-functionally: Work with data science and product teams to translate business requirements into performance and availability SLOs.

• 5+ years of strong development experience
• Experience deploying and operating LLM inference services in production.
• Strong production coding skills in Python plus Go or Rust (systems-level implementation and debugging).
• Experience with ML frameworks and runtimes: PyTorch, vLLM, SGLang  (and/or TensorRT).
• Knowledge of GPU architecture and performance (profiling, memory bandwidth/latency tradeoffs); CUDA/kernel programming is a strong plus.
• Solid understanding of LLM inference and optimization techniques: continuous batching, KV cache management, quantization, speculative decoding (nice-to-have), etc.
• 3+ years hands-on experience in performance optimization and systems programming for AI/ML workloads.
• Demonstrated ability to deliver measurable production improvements (e.g., 2X throughput, lower p95/p99 latency, reduced GPU cost).
• Proven skill in root-cause analysis: finding bottlenecks across model, runtime, networking, and infrastructure.

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