Remote Forward-Deployed AI Engineer (Customer Deployments)

Tenstorrent; Austin (TX, CA); Hybrid; USD 100,000 - 500,000; Full time

Job summary

Tenstorrent is seeking a Forward Deployed Engineer to build with engineers using Tenstorrent AI computers. You will connect customers, engineering, and inference service products; this is an engineering-first role with production code, deployments, and clear trade-offs.

Remote, NA-based with hubs in Santa Clara, Austin, or Toronto; open to varying experience levels with offers aligned to level during interviews.

Qualifications

• Strong software engineering with 5+ years of relevant experience.
• Experience turning ambiguous customer requirements into acceptance criteria.
• Kubernetes and Helm experience at multi-node, HPC, or AI cluster scale.
• Observability and infrastructure automation: Prometheus, Grafana, OpenTelemetry.
• Experience with LLM inference serving engines and technologies (e.g., vLLM, SGLang, Mooncake, NIM, Dynamo, LMCache).

Responsibilities

• Contribute production code and operate deployments.
• Explain trade-offs to customer leadership clearly and effectively.
• Create continuity between customers, engineering, and AI inference service products.
• Work directly with customers to understand challenges and provide effective solutions.
• Operate in a high-autonomy role with direct customer impact.

Skills

Strong software engineering

Customer-facing communication

Acceptance criteria derivation

Problem solving

Tools

Kubernetes

Helm

Prometheus

Grafana

OpenTelemetry

vLLM

SGLang

Mooncake

NIM

LMCache

Dynamo

Job description

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 isremote, 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 offers will align with that level, which may differ from the one in this posting.

Who You Are

• 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.

What We Need

• 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.
• Kubernetes and Helm experience at multi-node, HPC, or AI cluster scale.
• Experience with observability and infrastructure 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 asymmetric impact.

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.

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