[Hiring] Senior Software Engineer @Tensordyne

Role Description

Help make the latest AI models run well on our silicon. You’ll work from model code to measured hardware performance:

• Bring up new architectures, track down accuracy failures, and improve latency, throughput, and memory use.
• Build an agentic harness that can explore implementations, run experiments, check results, and guide the next change.
• Every investigation should leave behind better software, better tests, and a shorter path for the next model.

We’re looking for an excellent software engineer who understands LLM inference deeply and knows how to put coding agents to work. You’ll own substantial parts of our SDK and work closely with engineers across models, compilers, runtime, and hardware. Your particular focus will be numerical optimization:

• Finding where precision matters, where computation can be cheaper, and how to preserve model quality.

What you’ll do:

• Build the agentic workflows.
• Give agents the tools to inspect models, change implementations and numerical settings, run emulation and hardware experiments, and diagnose failures.
• Build the feedback and evaluation that make the results trustworthy.
• Bring up new models quickly; make them go brrrr.
• Work with engineers across the stack to take unfamiliar architectures from reference code to validated, performant execution on our accelerator.
• Turn what you learn into reusable model support and SDK improvements.
• Optimize numerics.
• Explore precision, accumulation, and quantization choices across the model.
• Understand how they interact with kernels, KV caches, memory, and distributed execution, then measure the gains in latency, throughput, and memory use against model quality on silicon.
• Make numerical quality measurable.
• Build conversion and emulation software that lets you test numerical choices quickly.
• Compare activations and outputs against reference models, trace accuracy regressions to their source, and validate changes with end-to-end model evaluations.
• Ship software that lasts.
• Own design, implementation, testing, and delivery.
• Build clear APIs that are easy to use for agents.
• Make difficult bugs reproducible and keep them from returning.
• Know when to build fast, and when to build thoroughly.
• Shape the next chip.
• Work with hardware architects to test how numerical formats and arithmetic choices affect real models.
• Turn those findings into better hardware and algorithm designs.

Qualifications

• You understand LLM inference end to end.
• You can read a new model’s implementation and reason about prefill, decode, attention, KV caches, MoE, batching, and parallelism.
• You understand how these translate into compute, memory, and communication on an accelerator.
• You build excellent software.
• You’ve owned substantial systems and delivered complex features from design through production.
• You’re strong in Python, thoughtful about interfaces, and rigorous about abstractions and testing.
• You are fluent with agentic tooling.
• You’d rather build the agent that runs the optimization than doing it by hand; and running numerous agents in parallel excites you.
• You know what is required for agents to work effectively in a large software organization, and you have the judgement when an agentic loop still needs a human.
• You take ownership and bring others along.
• You form clear hypotheses, test them, and drive problems to resolution across software boundaries.
• You communicate clearly through specs, design docs, and reviews that people and agents can act on.
• You explain complex trade-offs, give direct feedback, and make the people around you better.

Requirements

• Expertise in improving inference systems like debugging and optimizing the runtime of complex models and reasoning about accuracy-performance trade-offs.
• Experience with ML compilers, accelerator SDKs, or model compression libraries, particularly for non-NVIDIA accelerators such as TPU, Trainium/Inferentia, Tenstorrent, Groq, or Cerebras.
• Experience with model quantization, sparsity, codebooks, low-rank factorization or other compression techniques.
• Contributions to distributed inference or serving frameworks such as vLLM or SGLang.

Benefits

• Your code will help determine how quickly we support new models, how efficiently they run, and what we build into our next chip.
• You’ll own meaningful software, work directly with the people designing the hardware, and see your decisions tested on real silicon.
• If you want to build the tools, solve the hard failures, and help set the engineering direction of a new inference platform, we’d like to hear from you.

Company Description

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.