Job description

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 ML team builds and optimizes the models that run on SambaNova's RDU accelerators. Their work covers model architecture, training and fine-tuning, inference optimization, evaluation, and data curation, and it lands in SambaStack and SambaCloud. They work directly with the compiler, systems, and hardware teams on co-design, so decisions about a model shape decisions about the silicon it runs on. About the role As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models—with a focus on cutting-edge Large Language Models (LLMs)—to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation. Responsibilities Some of your responsibilities will include: Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale Identify, evaluate, and champion state-of-the-art ML techniques (e.g., speculative decoding, reinforcement learning, mixture-of-experts, long-context modeling) for adoption and adaptation on reconfigurable dataflow architectures Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud Drive resolution of the most complex, ambiguous technical challenges —including those that span organizational boundaries or require novel approaches not yet established in the field Required Qualifications B.S. in Computer Science, Electrical Engineering, or related field 8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects Preferred Qualifications M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field Experience with hardware-software co-design with non-GPU accelerators Publications or open-source contributions in LLM training or inference Experience with speculative decoding, mixture-of-experts, or long-context modeling in production Experience with reinforcement learning for post-training Familiarity with compiler or kernel-level optimization for ML workloads Base Salary Range: Base Pay Range $220,000 — $300,000 USD Submission 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.

Requirements

Must-haves

Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem
Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale
Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps
Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer
Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud
Drive resolution of the most complex, ambiguous technical challenges —including those that span organizational boundaries or require novel approaches not yet established in the field
B.S. in Computer Science, Electrical Engineering, or related field
8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems
Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale
Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art
Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority
Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects

Nice-to-haves

M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field
Experience with hardware-software co-design with non-GPU accelerators
Publications or open-source contributions in LLM training or inference
Experience with speculative decoding, mixture-of-experts, or long-context modeling in production
Experience with reinforcement learning for post-training
Familiarity with compiler or kernel-level optimization for ML workloads

Application prep

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Keywords to mirror

aimlmachine learningllm

Platform: greenhouse
Effort: quick

Benefits

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Dental

Vision