Staff Forward Deployed Engineer

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We’re looking for a Staff Forward Deployed Engineer who’s excited to build with the engineers using the AI computers Tenstorrent makes. You will create continuity between customers, engineering, and AI inference service products. This is an engineering role first: you contribute production code, operate deployments, and you can explain a trade-off to customer leadership as clearly as to core engineering teams. This is a high-autonomy role with direct customer impact. This role is remote, based out of North America, with preference near one of our main hubs: Santa Clara, CA; Austin, TX; or Toronto, ON. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and o [... source excerpt omitted ...] and don't treat hardware as a black box. You're an early adopter of AI for your work from coding to building agentic workflows that multiply your impact. You work directly with customers to understand their challenges and provide effective solutions. You are comfortable debugging across the full inference stack: from failing requests, through the serving layer, down to OOMs or kernel dispatch if need be. You bring feedback in the form of pull requests, reproducible code, benchmarks, and telemetry data. What We Need Strong software engineering skills with 5+ years of relevant technical experience (e.g. Applied Engineer, Machine Learning Engineer, MLOps Engineer, Platform En [... source excerpt omitted ...] ture automation, e.g. Prometheus, Grafana, OpenTelemetry. Experience with LLM inference serving engines and technologies, e.g. vLLM, SGLang, Mooncake, NIM, Dynamo, LMCache. What You Will Learn Where co-design of AI hardware and software translates into unique latency and throughput performance. How to scale disaggregated inference services on Kubernetes while balancing performance, reliability, and tactical tradeoffs. What makes enterprise AI deployments successful: from technical requirements through software delivery, cluster-scale validation, and production ownership. Why customer insights from the field shape the best products. How to build agentic workflows for asymme