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SambaNova Systems

Enterprise AI platform with hardware stack

AI Performance Engineer New Grad

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A Bachelor of Science degree in Computer Science, Electrical Engineering, or a related field is required.
  • Strong programming skills in Python or C++ are required.
  • Understanding of algorithms, data structures, and numerical computing is required.
  • Experience with PyTorch, TensorFlow, or JAX is required.
  • Experience with inference frameworks such as vLLM, TensorRT, or SGLang is required.
  • GPU programming experience with CUDA or Triton is required.

Responsibilities

  • Bring up new foundation models on the SambaNova platform using its 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.

Desired Qualifications

  • A Master of Science or Ph.D. in Computer Science, Electrical Engineering, or a related field is preferred.
  • Experience with large language model training or inference is preferred.
  • Familiarity with quantization, graph optimization, kernel fusion, or model partitioning is preferred.
  • Knowledge of memory hierarchy, caching, and scheduling for large model execution is preferred.
  • Internship or research experience in machine learning systems or performance optimization is preferred.
  • Publications or open source contributions in machine learning systems are preferred.

About the Company

SambaNova Systems

Enterprise AI platform with hardware stack

Generating company summary.

Company size
201-500
Company stage
Series F
Total funding
$2.5B
Headquarters
Palo Alto, CA
Founded
2017

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