Senior ML Infrastructure Engineer
Senior engineer builds and runs the production inference stack for SambaNova’s generative-AI platform, optimizing LLM serving on custom RDU hardware with low-latency, high-throughput systems.
- Compensation
- $200K–$275K / yr
- Location
- Austin, Texas, United States; San Jose, California, United States
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Full time
- Experience
- 5+ years
- Category
- Devops
- Posted
Skills
Must have
- B.S. in Computer Science, Electrical Engineering, or related field
- 5+ years of industry experience building and operating large-scale distributed systems, ideally in ML serving
- Strong software engineering fundamentals: algorithms, data structures, concurrency, and systems design
- Experience designing and maintaining production services with strict latency, throughput, and availability requirements
- Working knowledge of modern LLM inference techniques and familiarity with open-source serving stacks such as vLLM, TensorRT-LLM, or SGLang
- Proficiency in Python
- Experience collaborating across teams to deliver complex, system-level engineering solutions
Full 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 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.
About the role
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.
Responsibilities
- 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.
- Own SambaNova's integration with vLLM and adjacent serving frameworks, adapting them to RDU's architecture.
- Own the public inference API surface exposed through SambaStack and SambaCloud.
- Build and maintain the accuracy verification and regression infrastructure that gates every inference feature shipped to customers.
- Partner with ML, compiler, runtime, and product teams to take inference features from prototype to production.
- Contribute to technical design discussions, code reviews, and architectural decisions as a senior individual contributor.