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Tenstorrent is building cutting-edge AI hardware and software platforms, including a high-performance RISC-V CPU. The company is revolutionizing AI computing through innovations in semiconductors, compilers, platforms, networking, and software models.
As a Forward Deployed Engineer, you will own the technical outcomes of AI systems deployed with customers. You will work alongside customer engineering and operations teams to ensure complete systems function reliably in production or near-production environments. Your responsibilities span hardware and software integration, deployment, validation, automation, debugging, and ongoing operations. You will contribute production code, establish reliable operating procedures, and drive customer systems through resolution until they meet intended performance and reliability targets.
Key responsibilities include:
- Taking ownership of customer outcomes across system integration, validation, production readiness, and ongoing operations
- Understanding how accelerator compute, memory, and networking topology constrain AI workloads
- Working directly with customer engineering and operations teams to understand their environments and desired outcomes
- Debugging across the inference stack and communicating technical trade-offs with customer leadership and core engineering teams
- Contributing to production code and establishing reliable operating procedures
You will learn how co-design across AI hardware and software translates silicon capabilities into measurable latency and throughput gains, how to operate disaggregated inference systems on Kubernetes while balancing performance and cost, and how to take enterprise AI deployments from requirements through integration and production readiness.
The role is hybrid, based in Tokyo, Japan. Tenstorrent welcomes candidates at various experience levels; during the interview process, candidates will be assessed for the appropriate level, and offers will align accordingly.
**Requirements:**
- 5+ years of relevant experience in applied engineering, machine learning engineering, MLOps, platform engineering, infrastructure engineering, or site reliability engineering
- Strong software engineering skills with proficiency in Python; C++ experience is a plus
- Experience turning ambiguous requirements into production-ready implementations, acceptance criteria, validation plans, and operating procedures
- Experience with Kubernetes and Linux systems administration at multi-node, HPC, or AI-cluster scale, including Helm-based deployments, observability, infrastructure automation, CI/CD, release engineering, and LLM inference frameworks such as vLLM, SGLang, or Mooncake
- Bilingual: native or business-level Japanese with business-level English
