Technology Assistant

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’re looking for a 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: You understand how accelerator compute, memory, and networking topology constrain AI workloads, 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.

Strong software engineering skills with 5+ years of relevant technical experience (e.g. Applied Engineer, Machine Learning Engineer, MLOps Engineer, Platform Engineer, Infrastructure Engineer, Site Reliability Engineer, Field Application Engineer).

Experience turning ambiguous customer requirements or issues into verifiable acceptance criteria.

Experience with LLM inference serving engines and technologies, e.g. vLLM, SGLang, Mooncake, NIM, Dynamo, LMCache.

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.

S. export-controlled technology. S. export laws, including those codified in the U.Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.Commerce Department or applicable federal agency.