LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila is hiring an Associate Director/Director, Inference Engineering to lead the team responsible for Lila's model serving platform: the inference APIs, serving frameworks, request routing, batching strategies, and deployment systems that bring models reliably to production. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Deep understanding of the inference stack for large language models, including serving frameworks (e.g., vLLM, TensorRT-LLM, TGI), batching strategies, memory constraints, and latency/throughput tradeoffs
- Experience owning a customer- or product-facing API service end to end, including SLAs, incident response, and service reliability in fast-moving environments
- Track record hiring, mentoring, and retaining senior technical talent, and scaling engineering organizations through rapid growth
- Strong technical judgment across latency, throughput, reliability, cost, and delivery speed
- Demonstrated ability to set technical strategy and execute as priorities change
- Ability to align ML, research, product engineering, and executive stakeholders with competing priorities
More about this role
Lila is hiring an Associate Director/Director, Inference Engineering to lead the team responsible for Lila's model serving platform: the inference APIs, serving frameworks, request routing, batching strategies, and deployment systems that bring models reliably to production.
You will define how Lila serves models at scale, balancing latency, throughput, quality, and cost, and act as the technical authority connecting research and ML to production-grade serving capabilities. The work spans people leadership, service ownership, cross-functional partnership, and deep technical judgment in modern LLM serving.
- Own the inference service end to end: API contracts, latency SLAs, throughput guarantees, rate limits
- Drive serving architecture decisions: batching, KV cache, speculative decoding, quantization, multi-node deployment
- Establish SLOs for latency, throughput, and availability, plus on-call and postmortem practices
- Partner with ML, research, and product engineering to make new models production-ready
- Build systems for model onboarding, deployment safety, evaluation gates, and observability
- Build, mentor, and retain a high-performing engineering team, and own the serving...
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