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SambaNova Systems

engineering opportunity

Senior AI Systems Performance Engineer

The role involves optimizing and scaling state-of-the-art foundation models on the SambaNova dataflow platform by profiling performance across compiler, runtime, and hardware layers. Responsibilities include bringing up cutting-edge models, integrating architectural advances, and developing efficient end-to-end inference solutions.

San Jose, California, United StatesonsiteFULL_TIME

Posted

About the role

What will you do at SambaNova Systems?

The era of pervasive AI has arrived. In this era, organizations will use

generative AI to unlock hidden value in their data, accelerate processes, reduce

costs, drive efficiency and innovation to fundamentally transform their

businesses and operations at scale.

SambaNova Suite™ is the first full-stack, generative AI platform, from chip to

model, optimized for enterprise and government organizations. Powered by the

intelligent SN40L chip, the SambaNova Suite is a fully integrated platform,

delivered on-premises or in the cloud, combined with state-of-the-art

open-source models that can be easily and securely fine-tuned using customer

data for greater accuracy. Once adapted with customer data, customers retain

model ownership in perpetuity, so they can turn generative AI into one of their

most valuable assets.

ABOUT THE ROLE

We are seeking a talented and driven ML performance engineer to optimize and

scale state-of-the-art foundation models on SambaNova's reconfigurable dataflow

platform. You'll work hands-on with some of the most advanced models in the

world — such as DeepSeek R1, GPT OSS, and other frontier architectures — to push

the limits of throughput, latency, and efficiency. In this role, you'll bridge

the gap between deep learning and systems performance, collaborating across

compiler, runtime, and hardware layers to deliver world-record performance for

large-scale AI inference.

RESPONSIBILITIES

* Bring up and optimize cutting-edge foundation models (e.g., DeepSeek, Llama,

Qwen, and others) on the SambaNova platform through the SambaNova software

stack.

* Profile and enhance model performance across compiler, runtime, and hardware

layers to achieve SOTA throughput and latency.

* Collaborate with machine learning, compiler, runtime, and hardware teams to

deliver co-designed, high-performance AI applications.

* Integrate the latest advances in model architecture, quantization,

scheduling, and memory optimization from both academia and industry.

* Develop robust, scalable, and efficient end-to-end inference solutions

aligned with customer needs.

* Identify performance bottlenecks and propose dataflow or scheduling

optimizations for both single-node and distributed systems.

BASIC QUALIFICATIONS

* Bachelor's or higher degree in computer science, electrical engineering, or a

related field (e.g., applied mathematics, physics, or statistics).

* 3+ years of experience in one or more of the following areas:

* Deep learning model development and performance optimization

* Compiler, runtime, or kernel-level optimization

* Software–hardware co-design or systems performance tuning

* Proficiency in Python or C++, with strong foundations in algorithms, data

structures, and numerical computing.

* Experience with at least one major ML framework — PyTorch, TensorFlow, or

JAX.

* Demonstrated ability to analyze and optimize performance in real-world ML

pipelines.

PREFERRED QUALIFICATIONS

* Hands-on experience with LLM or multimodal model training and inference.

* Background in large-scale distributed training, continuous batching, and

high-throughput inference systems.

* Familiarity with quantization, graph optimization, kernel fusion, and model

partitioning.

* Experience with frameworks such as DeepSpeed, Megatron, vLLM, or TensorRT.

* Strong GPU programming skills (CUDA, Triton, or OpenCL); experience with

cuDNN, cuBLAS, or similar libraries is a plus.

* Knowledge of memory hierarchy optimization, caching, and scheduling for

large-scale model execution.

* Publication record or open-source contributions in ML systems or performance

optimization is a plus.

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.

Which skills does this role require?

Deep LearningPerformance OptimizationCompiler OptimizationRuntime OptimizationHardware OptimizationSystems Performance TuningPythonC++AlgorithmsData StructuresNumerical ComputingPyTorchTensorFlowJAXLLM InferenceGenerative AISambaNova SuiteSN40L ChipFoundation ModelsDeepSeek R1GPT OSSFrontier ArchitecturesLatencyEfficiencyDataflow PlatformCompilerRuntimeHardwareLlamaQwenMemory OptimizationDistributed SystemsNode.jsMachine LearningLLMs

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