Machine Learning Engineer - New Grad
About The Position
SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform centered on its Reconfigurable Dataflow Unit (RDU) and rack-scale SambaRack systems. The Machine Learning team develops model-side techniques that make the platform faster and more capable, spanning research, applied science, and production. As a new graduate Machine Learning Engineer, you will train and evaluate models that make large language models more efficient and capable to serve. Work may include speculative decoding, model compression, and agentic workflows for tool use, planning, and long-horizon tasks. You will collaborate with AI performance, compiler, runtime, and product teams to turn research results into measured end-to-end product improvements. The role also offers opportunities to contribute to reinforcement learning and post-training as those efforts expand.
Requirements
- 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.
Nice To Haves
- 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, including RLHF, DPO, GRPO, or reward modeling.
- Experience with distributed training frameworks such as FSDP, DeepSpeed, or Megatron, or inference frameworks such as vLLM, SGLang, or TensorRT-LLM.
- Publications at ML venues such as NeurIPS, ICML, ICLR, ACL, or EMNLP, or meaningful open-source contributions.
Responsibilities
- Train, evaluate, and tune speculative decoding models, including Dspark/Dflash, Medusa/Eagle-style heads, and multi-token prediction, to increase acceptance rates on production workloads.
- Develop and implement optimization techniques such as quantization, sparsity, distillation, pruning, and parameter-efficient fine-tuning, and quantify trade-offs between model quality, latency, and resource efficiency.
- Build and evaluate agentic systems involving tool calling, multi-step planning, retrieval, and long-context reasoning on the serving stack.
- Design rigorous evaluations and benchmarks, and ensure accuracy and quality claims hold up to scrutiny.
- Translate recent research into working implementations and run ablations to identify what drives results.
- Partner with 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.
Benefits
- Equity.
- 95% premium coverage for employee medical insurance.
- 77% premium coverage for dependent medical insurance.
- Health Savings Account (HSA) with employer contribution.
- Dental, vision, short- and long-term disability, basic life, voluntary life, and AD&D insurance plans.
- Flexible Spending Account (FSA) options for health care, limited purpose, and dependent care.
- Headspace subscription.
- Gympass+ membership with access to physical gyms.
- One Medical membership.
- Counseling services through an Employee Assistance Program.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
