Senior Principal Model Bring-up Engineer

About the team

We take frontier open-weight models (dense, mixture-of-experts with hundreds of experts, hybrid Mamba/attention, sliding-window attention, multi-token prediction) from Hugging Face to production serving on SambaNova's RDU dataflow accelerators. That means compiling them, proving them numerically correct against the reference, serving them with the full feature set, and merging the work into a shared stack so the next model is cheaper.

About the role

You will bring new open-weight LLMs, including dense, mixture-of-experts, and hybrid Mamba/attention architectures, up on SambaNova’s RDU dataflow accelerators and into production serving. That means adapting the model, mapping it across chips and hosts, compiling it, and validating it numerically against the reference. You also build inference features like continuous batching, speculative decoding, and FP8/FP4 as shared modules, so each new model costs less to bring up.

Responsibilities

Bring up new LLM architectures end to end: model adaptation, checkpoint conversion, multi-chip / multi-host mapping, compile, strict numerical validation, production serving with latency / throughput numbers.

Build inference features once as shared modules: continuous batching over a paged KV pool, prompt caching, chunked and incremental prefill, dynamic tensor shapes, speculative decoding (EAGLE-3, drafters, MTP), FP8 / FP4, tensor and expert parallelism.

Debug where model, compiler and hardware meet; land each fix in the right shared component.

Required qualifications

B.S. in Computer Science, Electrical Engineering, or related field

8+ years of industry experience in ML systems, inference, or compiler engineering

Strong Python and PyTorch internals experience, plus some C++

Deep transformer-inference knowledge, including KV caching, GQA and sliding window attention, MoE routing, prefill vs decode, batching, and speculative decoding

Hands-on experience with accelerator bring-up, ML compilers (MLIR, XLA, TVM, Triton, torch-mlir), or inference engines (vLLM, SGLang, TensorRT-LLM)

Rigor in numerical debugging

Comfort with Bazel, Slurm, and large monorepos

Clear writing and evidence-backed claims

Preferred qualifications

M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field

Experience with dataflow or spatial compilers

Experience with kernel DSLs

Experience with FP8 and FP4 inference

Experience with Mamba and SSMs

Experience with expert parallelism

Experience with multi-agent AI engineering workflows

Open-source inference or compiler contributions

Success in six months

Own a model family from first compile to served and merged; turn a one-model fix into a shared module others adopt; measurably shorten one long loop for the team.

Base Salary Range:

Base Pay Range

$208,000—$312,000 USD