Machine Learning Engineer, New Grad

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.

Key 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.

Requirements & Tech Stack

• 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.

Qualifications

• B.S. in Computer Science, Electrical Engineering, Mathematics, or a related field.
• Preferred: M.S. or Ph.D. in Computer Science, Electrical Engineering, or a related field.
• Preferred: Research or internship experience in LLM training, post-training, or inference.
• Preferred: Experience with speculative decoding, distillation, quantization, sparsity, or other model efficiency techniques.
• Preferred: Experience building agentic systems, tool-calling pipelines, or LLM evaluation harnesses.
• Preferred: Familiarity with reinforcement learning or preference optimization (RLHF, DPO, GRPO, reward modeling).
• Preferred: Experience with distributed training frameworks (FSDP, DeepSpeed, Megatron) or inference frameworks such as vLLM, SGLang, or TensorRT-LLM.
• Preferred: Publications at ML venues (NeurIPS, ICML, ICLR, ACL, EMNLP) or meaningful open source contributions.