Founding Engineer

We're looking for a Founding Engineer with deep AI infrastructure experience to design, build, and scale the systems that power our products. This is a hands-on, high-ownership role: you'll own everything from model serving and inference optimization to data pipelines, evaluation frameworks, and the cloud infrastructure underneath it all. Requirements: Architect and build the core AI infrastructure stack — model serving, inference pipelines, orchestration, and scaling Design and optimize LLM-based systems, including RAG pipelines, agentic workflows, fine-tuning infrastructure, and prompt/eval frameworks Build reliable, observable, cost-efficient inference systems (latency, throughput, and GPU utilization optimization) Stand up and own cloud infrastructure (AWS/GCP/Azure), CI/CD, containerization, and orchestration (Docker, Kubernetes) Build data pipelines for ingestion, processing, embedding, and retrieval at scale Establish engineering foundations — testing, monitoring, deployment practices, and security — that the team will build on for years Work directly with founders on product direction, technical strategy, and early customer deployments Help recruit, interview, and mentor the engineers who come after you What we're looking for: 4+ years of software engineering experience, with significant time spent on ML/AI infrastructure, platform engineering, or backend systems at scale Hands-on experience deploying and scaling LLM applications in production (model serving frameworks such as vLLM, TGI, Triton, or managed equivalents) Strong proficiency in Python; comfort with Go, Rust, or TypeScript is a plus Deep familiarity with cloud infrastructure (AWS, GCP, or Azure), Kubernetes, and infrastructure-as-code (Terraform or similar) Experience with vector databases, embedding pipelines, and retrieval systems Startup mindset: bias toward shipping, comfort with ambiguity, and willingness to work across the stack when needed Strong communication skills and the judgment to make pragmatic build-vs-buy tradeoffs Nice to have: Experience as an early or founding engineer at a startup GPU cluster management, distributed training, or inference optimization (quantization, batching, caching) Experience with agent frameworks, tool use / function calling, and multi-modal systems Prior work in fast-moving product environments with direct customer exposure #J-18808-Ljbffr