Machine Learning Engineer - New Grad

Sourced from feeny.ai

As a new grad Machine Learning Engineer on SambaNova's ML team, you'll train and evaluate models that make large language models cheaper and more capable to serve on the company's RDU-based inference platform. Work spans speculative decoding, model compression (quantization, sparsity, distillation), and agentic system evaluation, bridging research and production. You'll collaborate closely with AI performance, compiler, and runtime teams to turn research into measured end-to-end wins.

• Train, evaluate, and tune speculative decoding models (Medusa/Eagle-style heads, multi-token prediction) to increase acceptance rates on production workloads
• Develop optimization techniques including quantization, sparsity, distillation, pruning, and parameter-efficient fine-tuning
• Build and evaluate agentic systems: tool calling, multi-step planning, retrieval, and long-context reasoning
• Design rigorous evaluations and benchmarks with defensible accuracy and quality claims
• Translate recent research into working implementations and run ablations
• Partner with AI performance, compiler, runtime, and product teams to ship model improvements
• Contribute to reinforcement learning, post-training, and reward modeling as the roadmap expands

• 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 clean experiments, interpret results honestly, and communicate findings clearly

• 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