Inference Performance Engineer

Adaption is building efficient AI systems that adapt in real time. The Inference Performance Engineer will own the cost and performance of the inference stack, improving throughput, latency, and reliability through optimization of serving infrastructure, workloads, routing, and measurement systems.

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