Inference Engineer

The role

You'll own the cost and performance of our inference stack. Your work will determine how efficiently we serve models as workloads, traffic, and hardware change.

You'll work closely with the engineers operating the serving fleet while owning the core performance levers: caching, batching, quantization, decoding, and kernel-level optimization. Success means improving throughput and latency without compromising reliability or model quality.

Responsibilities

• Improve throughput, cost, and tail latency through KV-cache management, continuous batching, speculative decoding, and quantization.

• Optimize long-context prefill and decode workloads based on real production traffic.

• Tune routing between our infrastructure and external providers based on cost, capacity, and performance.

• Work within serving engines such as vLLM, SGLang, and TensorRT-LLM, going below the framework when needed.

• Build profiling and measurement systems that show where time, memory, and compute are being spent.

Qualifications

• 5+ years