About the Role:

In the Staff Engineer role, you will define and drive architecture for a high-throughput, low-latency, multi-tenant ML inference platform. You will balance hands-on coding with long-term technical direction, operate across ML Platform, infrastructure, MLE, and external-facing API needs, and establish principled architecture for serving, control plane, observability, capacity, tenant isolation, system economics, and model-engine integration.

Responsibilities:

• Design platform architecture for multi-tenant inference workloads across serving, orchestration, control plane, APIs, SDKs, observability, and model-engine integration.
• Develop robust API layers (gRPC, WebSockets, REST, etc.) and developer SDKs that abstract complex distributed inference orchestration into seamless, reliable token streams.
• Build and harden a multi-tenant control plane to enable accurate metering, rate limiting, quotas, tenant isolation and noisy-neighbor fairness across the platform.
• Optimize inference performance across the entire system stack, including the model engine layer.
• Build observability and SLOs to gain insights into system economics, cache-hit rates, GPU utilization and cost accounting per model and per tenant.
• Partner with product and infrastructure teams on model onboarding, capacity planning, external API contracts and customer adoption.
• Promote Engineering Excellence: Maintain a high bar for engineering excellence in their own work but also set a culture of engineering excellence within the team.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience: 7+ years of experience building and operating backend distributed systems end to end.
• Demonstrated cross-team technical leadership in backend distributed systems, ML infrastructure, inference serving, or high-performance compute platforms.
• Strong Data & ML systems fundamentals: data-intensive distributed systems, concurrency, networking and performance profiling.
• Hands-on experience running large-scale inference services on GPUs, including KV caches, prefill/decode stages and throughput/latency trade-offs.
• Direct experience with inference engines (TensorRT, vLLM, etc) or serving frameworks (Dynamo, Triton or equivalent).
• Technical Skills:
  • Strong programming skills in C++, Go, Rust or Python.
  • Familiarity with deep learning frameworks (PyTorch, etc.) as well as model parallelism.
  • Familiarity with GPU computing primitives such as CUDA, NCCL, NVLink, and hardware-specific optimizations.
  • Practical understanding of high-performance networking architectures, including InfiniBand, RoCE, and low-latency cluster communication.
• Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
• Autonomous vehicles (AV) experience is a bonus.