Forward Deployed Inference Engineer

Gudejobs; München; Hybrid; EUR 110.000 - 170.000; Vollzeit

Zusammenfassung

Tensordyne is seeking a Forward Deployed Inference Engineer in Munich to bridge customer workloads and the Tensordyne platform. You will work hands-on with AI systems and collaborate across product, BizDev, and engineering.

You will optimize model performance, run profiling, and help deploy PoCs with customers, translating requirements into measurable KPIs. This role blends systems engineering with direct customer impact.

Qualifikationen

• Hands-on experience with AI models and inference systems, including dense or MoE LLMs and VLMs.
• Strong Python and PyTorch coding ability to understand and modify model code.
• Experience profiling, benchmarking, or optimizing model inference for latency and throughput.
• Excellent problem-solving and communication to drive complex problems across teams.
• Familiarity with AI-focused developer tools (Claude Code, Cursor).

Aufgaben

• Turn customer workloads into fast, credible performance answers via profiling and benchmarking.
• Enable and optimize models on the Tensordyne stack; validate numerical quality and bottlenecks.
• Collaborate with customers on PoCs, integration, deployment, and defining acceptance KPIs.
• Track profiling-to-hardware accuracy and flag gaps in compiler, SDK or deployment stacks.
• Create reusable tooling, docs, benchmarks, and product improvements from learnings.

Kenntnisse

AI models & inference systems

Python & PyTorch

Profiling, benchmarking & optimization

Cross-functional collaboration

Develop tools / AI-powered developer

Tools

Rust

Kubernetes

Jobbeschreibung

About Tensordyne

Tensordyne is building a new class of AI inference system designed for high-performance, power-efficient deployment of the world’s most demanding generative AI workloads.

Our platform combines purpose-built silicon, new AI math, optimized scale-up networking, and memory architecture into a tightly integrated system purpose built for large-scale AI inference. We work with hyperscalers, Neoclouds, frontier model developers, enterprises, and infrastructure partners operating at the leading edge of AI.

As Tensordyne moves from system development into silicon bring-up, customer validation, beta deployments, and production rollout, we are building the technical customer organization that will sit directly between our engineering teams and the companies deploying the platform.

Role summary

We are looking for a Forward Deployed Inference Engineer who combines deep AI systems expertise with strong customer instincts. This person will own the path from a customer workload or model request to a technical result and, where needed, to an optimized model running successfully on Tensordyne hardware and software.

The role sits at the intersection of model architecture, inference performance, systems optimization, developer tooling, and customer deployment. You will work hands-on with engineering while also acting as a technical bridge to Product, BizDev, Sales, and customers.

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.