Machine Learning Engineer - New Grad

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) or meaningful open source contributions

Base Salary Range:

Base Pay Range

$131,200—$196,800 USD