AI/Backend Engineer | San Francisco | $250k + equity

Harrison Clarke are partnered with an early-stage startup building ground truth infrastructure for AI agents - creating the data, evaluation, and runtime systems that allow LLM-powered agents to behave reliably in real-world environments. As an AI / Infrastructure Engineer focused on LLM systems, you will help design and operate the production backbone for deploying and scaling large language models. This includes building low-latency inference systems, GPU-optimised serving infrastructure, and the evaluation pipelines that ensure model outputs remain accurate, consistent, and grounded. Key Responsibilities Design and operate infrastructure for deploying LLMs (e.g., GPT-style, open-weight, fine-tuned models) Build and optimise high-throughput, low-latency inference pipelines Implement scalable LLM serving systems (batching, caching, streaming, request routing) Manage GPU-based infrastructure with a focus on cost and performance efficiency Deploy and maintain model serving stacks (e.g., vLLM, TensorRT-LLM, TGI, Triton, or equivalents) Build systems for model routing, fallback logic, and multi-model orchestration Implement observability for LLM systems (latency, throughput, cost, failure modes, quality signals) Collaborate with ML and product teams to productionise RAG systems and fine-tuned models Qualifications 3+ years in infrastructure engineering, MLOps, or backend systems roles Proven experience deploying ML or LLM systems in production environments Strong proficiency in Python and/or Go Strong understanding of distributed systems and scalable backend architecture Hands-on experience with Docker, Kubernetes and CI/CD pipelines Familiarity with model serving frameworks (e.g., vLLM, Triton, TGI) Experience building high-performance APIs for production systems Strong debugging skills across infrastructure and application layers Must have the legal right to work in the US and must not require visa sponsorship #J-18808-Ljbffr Harrison Clarke