Inference Engineer

Adaption builds efficient AI systems that adapt in real time. The Inference Engineer will own the cost and performance of the inference stack, improving throughput, latency, and reliability through serving optimization, profiling, routing, caching, batching, quantization, decoding, and kernel-level work.

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