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Forward Deployed Engineer

Tenstorrent
Austin, USA
Full-time
Mid
Hybrid
USD 100000-500000 yearly / year
Discovered Yesterday
LinuxPythonC++

Full Job 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.

This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. 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). These requirements apply to persons located in the U.S. and all countries outside the U.S. 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.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.

Requirements

  • 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.
  • 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.
  • 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.

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