SambaNova Machine Learning Engineer New Grad — Austin or San Jose

Key Facts

Overview

SambaNova builds a full-stack inference platform around its Reconfigurable Dataflow Unit. The Machine Learning team trains speculative-decoding models, works on quantization, sparsity, and distillation, and builds agentic systems on the serving stack. Reinforcement learning and post-training are part of the roadmap.

New-grad ML engineers train and evaluate models that make large language models cheaper and more capable to serve: speculator models and acceptance rates, compression without losing accuracy, or agentic workflows for tool use, planning, and long-horizon tasks. The role works with AI performance, compiler, and runtime teams.

Eligibility / Key Requirements

• B.S. in computer science, electrical engineering, mathematics, or a related field
• Strong Python
• Machine learning, linear algebra, probability, and numerical computing
• Hands-on PyTorch, TensorFlow, or JAX
• Working knowledge of transformers and the LLM training or inference pipeline
• Preferred: M.S. or Ph.D.; LLM training, post-training, or inference; speculative decoding, quantization, or sparsity; agentic systems; RL or preference optimization; distributed training or vLLM / SGLang / TensorRT-LLM

How to Apply

1. Open the official application from EduBard’s Apply button.
2. Choose the Austin or San Jose location on the employer form.
3. Confirm the base range and start date on the posting before you submit.

Compiled from the official posting; verify details on the employer site before applying.