Forward Deployed Inference Engineer

What you will do

• Turn customer workloads into fast, credible performance answers through profiling & benchmarking . Define relevant KPIs, compare against competitive baselines, and keep our evaluation methodology current with external benchmarks.
• Model enablement & optimization: convert and bring up customer models on the Tensordyne stack, validate numerical quality, identify performance bottlenecks, and work with compiler, runtime, kernel, and system teams to improve results.
• Deployment / forward engineering: work directly with customers and partners on technical PoCs, integration, deployment, and debugging; translate requirements into measurable acceptance criteria for quality, latency, throughput, and other relevant KPIs.
• Track profiling-to-hardware accuracy by continuously comparing profiling/simulation results with actual hardware deployments, explain material gaps, and flag missing capabilities in the compiler, SDK, inference server, KV-cache management, or adjacent systems to the owning teams.
• Turn repeated customer-specific learnings into reusable tooling, documentation, benchmarks, or product improvements.

Core qualifications

• Strong hands-on experience with AI models and inference systems , especially dense and MoE LLMs (Llama, DeepSeek, Qwen, GPT-OSS, Kimi, GLM), and VLM, speech and diffusion models.
• Strong Python and PyTorch skills and the ability to understand and modify model code.
• Experience profiling, benchmarking, or optimizing model inference and reasoning about latency, throughput, memory, and utilization.
• Strong problem-solving and communication skills, with the ability to drive ambiguous technical problems across team boundaries.
• Proficiency in using AI-powered developer tools (e.g., Claude Code, Cursor).

Strong pluses

• Experience with LLM serving and deployment stacks such as vLLM, SGLang, or similar systems.
• Experience working directly with customers or external technical partners .
• Experience bringing models up on new accelerators or non-standard hardware , including performance debugging across framework/runtime/hardware boundaries.
• Practical experience with production inference techniques or environments such as quantization, distributed inference, or Kubernetes .
• Experience navigating and contributing to Rust codebases.

Tensordyne's culture was built on the following values

• Put people first. We only succeed when our people succeed.
• Ethics and integrity always; Being open, honest, and respectful of everyone.
• Think Big. Be ambitious and have audacious goals.
• Aim for excellence. Quality and excellence count in everything we do.
• Own it and get it done. Results matter!
• Make each person better together, than they would be as an individual.
• Embrace each others’ differences, and embrace that there will be differences.

Tensordyne is an equal opportunity employer. We believe diverse teams are better equipped to solve complex problems and build exceptional technology. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.

A note to recruitment agencies: Please do not contact Tensordyne employees or leaders regarding this role. We do not accept unsolicited agency resumes and are not responsible for fees associated with unsolicited submissions.

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Veröffentlicht am 01. Nov. 2026

Veröffentlicht am 01. Nov. 2026