<div class="content-intro"><p style="text-align: left;"><span style="font-size: 12pt;">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.</span></p></div><h3><strong>About the team</strong></h3> <p>The ML Infrastructure team builds and operates the inference stack that serves SambaNova's models on RDU accelerators, from request scheduling and caching through the public APIs in SambaStack and SambaCloud. We take inference techniques like speculative decoding, constrained decoding, and long-context serving from prototype to production, and own the accuracy infrastructure that gates every feature we ship. We work alongside the ML, compiler, runtime, and product teams, since most of what we build touches all four.</p> <h3><strong>About the role</strong></h3> <p>The Senior Software Engineer, ML Infrastructure will be responsible for designing, building, and operating the production-grade inference infrastructure that powers SambaNova's serving stack on our Reconfigurable Dataflow Unit (RDU) architecture. SambaNova is an inference-first company, and this role sits at the heart of that mission: turning state-of-the-art inference techniques into reliable, high-throughput, low-latency services exposed to customers through SambaStack and SambaCloud. The engineer will own end-to-end systems spanning request scheduling, advanced decoding algorithms, caching layers, API surfaces, and the accuracy infrastructure that keeps the stack trustworthy. This role partners closely with ML, compiler, runtime, and product teams to ship inference features from prototype to production.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li>Design and productionize advanced inference techniques on RDU to optimize for performance and cost. Key areas include speculative decoding, constrained decoding, function/tool calling, prompt caching, and long-context inference.</li> <li>Own SambaNova's integration with vLLM and adjacent serving frameworks, adapting them to RDU's architecture.</li> <li>Own the public inference API surface exposed through SambaStack and SambaCloud.</li> <li>Build and maintain the accuracy verification and regression infrastructure that gates every inference feature shipped to customers.</li> <li>Partner with ML, compiler, runtime, and product teams to take inference features from prototype to production.</li> <li>Contribute to technical design discussions, code reviews, and architectural decisions as a senior individual contributor.</li> </ul> <h3><strong>Required Qualifications</strong></h3> <ul> <li>B.S. in Computer Science, Electrical Engineering, or related field</li> <li>5+ years of industry experience building and operating large-scale distributed systems, ideally in ML serving</li> <li>Strong software engineering fundamentals: algorithms, data structures, concurrency, and systems design</li> <li>Experience designing and maintaining production services with strict latency, throughput, and availability requirements</li> <li>Working knowledge of modern LLM inference techniques and familiarity with open-source serving stacks such as vLLM, TensorRT-LLM, or SGLang</li> <li>Proficiency in Python</li> <li>Experience collaborating across teams to deliver complex, system-level engineering solutions</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>Base Salary Range:</p></div><div class="title">Base Pay Range</div><div class="pay-range"><span>$200,000</span><span class="divider">—</span><span>$275,000 USD</span></div></div></div><div class="content-conclusion"><p><strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Submission Guidelines<br></span></strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">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. </span></p> <p><strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">EEO Policy<br></span></strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">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.</span></p> <p style="text-align: left;"><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;"><strong>Benefits Summary for US-Based, Full-Time Employment Positions</strong><br>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.</span></p></div>