Senior Principal Model Bring-up Engineer
SambaNova Systems
$208,000 - $312,000 / year
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Description
SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova's technology is the RDU (Reconfigurable Dataflow Unit) — a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value.
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:
Submission Guidelines
Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.
EEO Policy
SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Benefits Summary for US-Based, Full-Time Employment Positions
SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.
Benefits
- health insurance
- stock options
About this role
SambaNova is building the next generation of AI infrastructure around dataflow accelerators, and this role puts you at the center of making that hardware useful. You'll take cutting-edge open-weight models—dense networks, mixture-of-experts variants, Mamba hybrids, and beyond—and bring them from research to production serving on SambaNova's RDU chips. That means owning the full pipeline: model adaptation, multi-chip mapping, compilation, strict numerical validation, and latency optimization. You'll also build reusable inference features like continuous batching, speculative decoding, and low-precision quantization as shared modules, so each subsequent model costs less to ship.
This is a deep systems role that demands rigor at the intersection of models, compilers, and hardware. You need 8+ years in ML systems, inference, or compiler engineering, strong Python and PyTorch internals, and real hands-on experience with accelerator bring-up or inference frameworks like vLLM or TensorRT-LLM. Transformer inference knowledge is table stakes—KV caching, GQA, MoE routing, batching strategies, speculative decoding. You should be comfortable debugging where model, compiler, and hardware meet, and disciplined enough to land fixes in the right shared component rather than one-off patches.
Success here means shipping a model family end to end within six months, converting single-model fixes into reusable modules that the team adopts, and measurably shortening critical loops. The role is hybrid in Austin, with a base salary of $208k–$312k and a benefits package covering medical, dental, vision, disability, and wellness services including Headspace and One Medical.
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Pay for this role
$208,000 to $312,000 per year
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