5+ years in systems or ML systems, with expertise in ML compilers, model porting, or high-performance kernels.
Experience running a model architecture on a target it wasn't designed for, with strong numerics debugging skills.
Deep understanding of modern LLM inference, including transformers, attention mechanisms, and quantization.
Proficient in compiler stacks like MLIR/LLVM or XLA, focusing on IR and op coverage.
Ability to build agentic tooling for automating the bring-up process, with a keen sense of necessary human intervention.
Self-directed and proactive in anticipating and addressing upcoming model challenges.
Responsibilities
Own the end-to-end model bring-up process for new architectures, ensuring rapid deployment on ASICs.
Develop an agentic bring-up loop that improves efficiency with each new model iteration.
Engage deeply with the compiler to resolve issues related to graph capture, IR lowering, and kernel selection.
Establish a verification harness to ensure correctness before deployment, using layer-by-layer activation diffs and end-to-end evaluations.
Optimize models post-verification for performance, focusing on operator fusion and memory management.
Collaborate closely with hardware partners to transition models from theoretical capability to live production.
Benefits
Opportunity to work on cutting-edge AI models and architectures.
Collaborative environment with a small, focused team.
Autonomy in managing projects without micromanagement.
Access to advanced tools and technologies in ML and compilers.
Potential for professional growth in a rapidly evolving field.
Full Job Description
About the role
You'll take a new model and get it running - correctly - on our ASIC in record time. When a frontier model drops, the only question that matters is how fast we can land it on our silicon and start serving it. You own that loop: from reference weights, through the compiler, to first correct tokens. The low-level runtime is co-owned with our hardware partner today; your job is everything it takes to get a brand-new architecture compiled, verified, and fast on top of it.
The bet of this role is that bring-up should be an agentic loop, not a hand-port. You'll build the harness of agents that compiles, runs, diffs against reference, and localizes failures - so the marginal model comes up faster than the last one did. Correctness first, optimization second: get it right, prove it's right, then make it cheap. This is a senior IC role on a small team. You'll own the bring-up pipeline, not tickets.
What You'll Do:
Own model bringup end-to-end. Take a new architecture - a frontier LLM, an MoE, a multimodal model - from reference weights to first correct tokens running on our ASIC, in days, not quarters.
Build the agentic bringup loop. The differentiator isn't hand-porting one model - it's the harness of agents that compiles, runs, diffs against reference, localizes the failing op, and iterates without you in the inner loop. Each model you land should make the loop better at landing the next one.
Live in the compiler. Graph capture, IR lowering, op coverage, kernel selection - when a model won't compile or produces wrong numbers, the fix is yours, whether it's a missing lowering, a fused-kernel bug, or a numerics mismatch.
Own correctness before speed. Build the verification harness - layer-by-layer activation diffs, logit parity, end-to-end evals - that proves a freshly brought-up model matches reference before anyone trusts a token of it.
Then optimize. Once it's correct, make it fast: operator fusion, quantization, memory layout, batching and KV-cache behavior on our hardware. Bringup gets it running; this is where it earns its cost-per-token.
Work shoulder-to-shoulder with our hardware partner's compiler and runtime team. You're the person who turns 'the chip can technically run this' into 'this model is live and correct in production.
What we need from you:
5+ years in systems or ML systems, with real depth in at least one of: ML compilers, model porting/bringup, or high-performance kernels.
You've taken a model architecture you didn't design and made it run - and run correctly - on a target it wasn't written for. Numerics debugging doesn't scare you.
Strong on the internals of modern LLM inference: transformers, attention, KV cache, MoE routing, quantization, batching. You can read a new model's reference implementation and know what will be hard to lower.
Comfortable inside a compiler stack - MLIR/LLVM, XLA, or a vendor graph compiler - at the level of IR, lowering, and op coverage, not just calling into one.
Fluent with agentic tooling. You'd rather build the agent that runs the tedious bringup loop than run it by hand - and you have the taste to know where the loop still needs a human.
Self-directed. We don't assign tickets - you'll see the next model coming and have it half brought-up before anyone asks.
Nice-to-haves:
Have worked on a non-NVIDIA accelerator - TPU, Trainium/Inferentia, Tenstorrent, Groq, Cerebras, or similar - at the compiler or model-bringup layer.
Kernel-level experience in CUDA, Triton, or a vendor kernel language. You know why a fused attention kernel beats three unfused ops.
Have built eval and numerics-verification harnesses (logit parity, activation diffing) for models in production.
Contributed to a graph compiler or serving runtime - XLA, TVM, MLIR, vLLM, TGI, TensorRT-LLM, or SGLang.
Have built agent loops or LLM-driven tooling that did real engineering work, not demos.
