# Machine Learning Engineer - New Grad at SambaNova Systems

SambaNova Systems is hiring a Machine Learning Engineer - New Grad in Austin and San Jose, United States. It pays $131k-$197k a year and Level rates it Builds AI ●●●●; you can [apply on SambaNova Systems](https://jobsbylevel.com/go/e22741c8-4576-48e2-90aa-edb7802112da?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. Austin, Texas, United States; San Jose, California, United States.

## Details

- Company: [SambaNova Systems](https://jobsbylevel.com/companies/sambanova-systems)
- AI level: AI Level 4 (score 100 out of 100)
- Location: Austin, Texas, United States; San Jose, California, United States
- Salary: $131k-$197k
- Posted: November 3, 2026
- Apply: https://jobsbylevel.com/go/e22741c8-4576-48e2-90aa-edb7802112da?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=machine-learning-engineer-new-grad-at-sambanova-systems-2b19de

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/machine-learning-engineer-new-grad-at-sambanova-systems-2b19de

## Description

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 Machine Learning team builds the model-side techniques that make SambaNova’s platform fast and capable. We train speculative decoding models that accelerate inference, develop efficient ML methods such as quantization, sparsity, and distillation, and build agentic systems on top of our foundation model serving stack. Our work spans research, applied science, and production, from novel approach design to proofs of concept and shipping to product. Reinforcement learning and post-training are within our scope and a growing part of our roadmap. About the Role As a new grad Machine Learning Engineer, you’ll train and evaluate models that make large language models cheaper and more capable to serve. Depending on your interests, that could mean training speculator (draft) models and tuning acceptance rates, compressing models without losing accuracy, or designing and evaluating agentic workflows for tool use, planning, and long-horizon tasks. You’ll work closely with the AI performance, compiler, and runtime teams to turn research results into measured end-to-end wins, and you’ll have room to pursue open problems as our reinforcement learning and post-training work ramps up. Responsibilities Train, evaluate, and tune speculative decoding models (Dspark/Dflash, Medusa/Eagle-style heads, and multi-token prediction) to increase acceptance rates on production workloads. Develop and implement optimization techniques, including quantization, sparsity, distillation, pruning, and parameter-efficient fine-tuning, to quantify the trade-offs between model quality, latency, and resource efficiency. Build and evaluate agentic systems: tool calling, multi-step planning, retrieval, and long-context reasoning on top of our serving stack. Design rigorous evaluations and benchmarks; make accuracy and quality claims that hold up under scrutiny Translate recent research into working implementations, and run ablations to understand what actually drives the result Partner with the AI performance, compiler, runtime, and product teams to ship model improvements into SambaNova products Contribute to emerging areas such as reinforcement learning, post-training, and reward modeling as the roadmap expands Required Qualifications B.S. in Computer Science, Electrical Engineering, Mathematics, or a related field Strong programming skills in Python Solid foundation in machine learning, linear algebra, probability, and numerical computing Hands-on experience with PyTorch, TensorFlow, or JAX Working knowledge of transformer architectures and the LLM training or inference pipeline Ability to design a clean experiment, interpret results honestly, and communicate findings clearly Preferred Qualifications M.S. or Ph.D. in Computer Science, Electrical Engineering, or a related field Research or internship experience in LLM training, post-training, or inference Experience with speculative decoding, distillation, quantization, sparsity, or other model efficiency techniques Experience building agentic systems, tool-calling pipelines, or LLM evaluation harnesses Familiarity with reinforcement learning or preference optimization (RLHF, DPO, GRPO, reward modeling) Experience with distributed training frameworks (FSDP, DeepSpeed, Megatron) or inference frameworks such as vLLM, SGLang, or TensorRT-LLM Publications at ML venues (NeurIPS, ICML, ICLR, ACL, EMNLP)

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