Strong fit for new grad candidates with a research-oriented mindset who can work on model efficiency techniques and agentic systems, and who are comfortable with collaborative, cross-functional teams in the US.
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Original posting from SambaNova
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) or meaningful open source contributions
Base Salary Range:
Base Pay Range$131,200—$196,800 USDSubmission Guidelines
Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.
EEO Policy
SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Benefits Summary for US-Based, Full-Time Employment Positions
SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.
Source: SambaNova careers