Senior AI Performance Engineer
SambaNova Systems is hiring a Senior AI Performance Engineer in San Jose, United States. It pays $180k-$255k a year and Level rates it ; you can apply on SambaNova Systems.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- The posting mentions Llama, Deepseek and vLLM among its tools and skills.
- Level classes it as a senior role.
- SambaNova Systems has 71 other open roles on Level.
AI in this role
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 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.
Required 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.
Base Salary Range:
Base Pay Range$180,000—$255,000 USDSubmission 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.
How we rate this
Senior AI Performance Engineer at SambaNova Systems rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used Llama hands-on?
- Walk me through how you've used Deepseek in your day-to-day work.
- What are the limits of vLLM that you've run into, and how did you work around them?
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Llama, Deepseek, vLLM, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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