Forward Deployed Engineer

We’re looking for a Forward Deployed Engineer to own the technical outcome of the AI Hardware systems we build with our customers. You will work alongside customer engineering and operations teams to make complete systems work in production or near-production environments. This engineering role spans hardware and software integration, deployment, validation, automation, debugging, and ongoing operations. You will contribute to customer deployments, establish reliable operating procedures, and drive customer systems through resolution until they work as intended.

This role is hybrid, based out of Santa Clara, California, Austin, Texas or Toronto, Canada.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• You take ownership of customer outcomes in deployed environments through integration, validation, production readiness, and operations.
• You understand how accelerator compute, memory, and networking topology constrain AI workloads, and don’t treat hardware as a black box.
• You work directly with customer engineering and operations teams to understand their environment, constraints, and desired outcomes.
• You’re comfortable debugging across the inference stack, from failing requests, kernel dispatch, drivers, networking, or hardware behavior.
• You automate repetitive work and communicate technical trade-offs clearly with both customer leadership and core engineering teams.

What We Need

Minimum Requirement

• Hands-on experience with Server hardware in a Datacenter environment. 
• Experience troubleshooting complex issues with hardware and software. 
• Experience with Linux systems administration, including knowledge of debugging OS issues.
• Previous experience in one of the following roles: Site Reliability Engineer, Systems Engineer, Platform Engineer, or Infrastructure Engineer.

Preferred requirement 

• Experience working with and troubleshooting GPUs, PCIe devices, or large clusters. 
• Strong software engineering skills in Python; C++ experience is a plus.
• Experience turning ambiguous requirements into production-ready implementations, acceptance criteria, validation plans, and operating procedures.
• Kubernetes and Linux systems administration experience at multi-node, HPC, or AI cluster scale, including Kubernetes orchestration. 
• Experience with observability, infrastructure automation, CI/CD, release engineering, and LLM support.
• A desire to travel in the region to support deployments is a strong plus. 

What 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, reliability, maintainability, and cost.
• How to take enterprise AI deployments from requirements through integration, software delivery, validation, production readiness, and ongoing ownership.
• How customer deployment and operational insights shape product improvements and platform direction.

Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.