SambaNova SystemsAI Performance Engineer - New Grad
SambaNova Systems is hiring an AI Performance Engineer - New Grad in Austin and San Jose, United States. It pays $131k-$197k a year and Level rates it ; you can apply on SambaNova Systems.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include Llama, Deepseek and vLLM.
- Level classifies it as a junior role.
- Level lists 59 other open roles at SambaNova Systems.
- The role was posted in the last few days.
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 Team
The AI Performance team brings up and optimizes foundation models such as DeepSeek, Llama, Qwen, and GPT OSS on SambaNova's dataflow platform. We profile and tune models across the compiler, runtime, and hardware layers to improve inference throughput and latency.
About the Role
As a new grad AI Performance Engineer, you'll bring up new open source models on the SambaNova stack and work to make them run faster. You'll profile model execution, find bottlenecks, and apply optimizations such as quantization, kernel fusion, and improved scheduling with the compiler, runtime, and hardware teams.
Responsibilities
- Bring up new foundation models on the SambaNova platform using our software stack
- Profile model performance across the compiler, runtime, and hardware layers
- Identify bottlenecks and propose dataflow or scheduling optimizations for single-node and distributed systems
- Apply techniques from recent research in quantization, scheduling, and memory optimization
- Benchmark throughput and latency and track performance changes across releases
- Work with the machine learning, compiler, runtime, and hardware teams to ship performance improvements into SambaNova products
Required Qualifications
- B.S. in Computer Science, Electrical Engineering, or related field
- Strong programming skills in Python or C++
- Understanding of algorithms, data structures, and numerical computing
- Experience with PyTorch, TensorFlow, or JAX
- Experience with inference frameworks such as vLLM, TensorRT, or SGLang
- GPU programming experience with CUDA or Triton
Preferred Qualifications
- M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field
- Experience with LLM training or inference
- Familiarity with quantization, graph optimization, kernel fusion, or model partitioning
- Knowledge of memory hierarchy, caching, and scheduling for large model execution
- Internship or research experience in ML systems or performance optimization
- Publications or open source contributions in ML systems
Base Salary Range:
Base Pay Range$131,200—$196,800 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
AI Performance Engineer - New Grad at SambaNova Systems rates 96 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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