LLM Inference Engineer (Mid, Senior, Staff)

Description du poste

As HAI’s LLM Inference Engineer, you will own the serving infrastructure that determines whether our breakthrough healthcare AI reaches patients efficiently and reliably You’ll optimize the systems that translate raw model capability into sub-100ms responses—making the difference between conversational experiences that feel natural and those that feel broken This role exists because inference optimization at scale is where research meets reality: your work directly determines latency, cost, and availability for millions of patient conversations across healthcare systems Own your first major outcome: By day 90, you will have shipped a measurable improvement to our inference serving stack (reduced latency, improved throughput, or optimized cost per inference), validated the gains across our production deployment scenarios, and established the performance optimization roadmap that will guide infrastructure investment Drive lasting impact: At 12 months, you will have designed and deployed advanced serving architectures (disaggregated inference, optimized caching, speculative decoding) that meaningfully improve patient experience and operational efficiency, contributed novel optimization techniques that become part of our core infrastructure, and made our serving stack a durable competitive advantage in healthcare AI deployment You’ll work alongside systems engineers, ML researchers, and infrastructure experts who are obsessed with making AI systems fast, reliable, and cost-effective. This is a team that values deep technical rigor, continuous benchmarking, and solving hard systems problems that have real impact on patient experience and business unit economics Design and implement multi-node serving architectures for distributed LLM inference Optimize multi-LoRA serving systems Apply advanced quantization techniques (FP4/FP6) to reduce model footprint while preserving quality Implement speculative decoding and other latency optimization strategies Develop disa

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