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SambaNova Systems

Enterprise AI platform with hardware stack

Principal Product Manager

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeSan Jose, CA, USAIn-person

About the Job

Requirements

  • A bachelor's degree or higher in Computer Science, Engineering, Machine Learning, or a related technical field.
  • At least 7 years of combined experience in product management and technical roles, including at least 3 years as a product manager on AI/ML, developer, or infrastructure products.
  • Strong working knowledge of large language models, including transformer and mixture-of-experts architectures, inference trade-offs such as latency, throughput, context, and quantization, and features such as function calling, structured output, and reasoning.
  • A track record of owning a roadmap and shipping, including setting priorities under constraints, making data-backed decisions, and driving cross-functional teams to launch.
  • Comfort with data, including building and maintaining prioritization models, dashboards, and scorecards, and the ability to query an API or modify a Python script.
  • Hands-on ability to call model APIs, run evaluations and quick experiments, and build simple prototypes or mock-ups using Python, notebooks, lightweight user interface tools, or AI coding assistants.
  • Ability to present a roadmap and defend trade-offs to executives, engineers, and customers.

Responsibilities

  • Own the unified, weighted stack rank of candidate models across customer demand, capability, market adoption, recency, strategic value, and engineering effort; tune the rubric, document overrides, and run regular prioritization reviews.
  • Own the plan of models, hardware generations, configurations, and performance targets for each chip, grounded in measured platform data.
  • Turn priorities into dated engineering commitments, track delivery, and run model launches with Marketing, Documentation, and go-to-market teams.
  • Keep the model roadmap and dashboard current as the source of truth for executives, account teams, and customers, and present it internally and externally.
  • Define what constitutes a fully featured model across speed, efficiency, model infrastructure, cloud, and application programming interface features, including tool calling, JSON schema and constrained decoding, reasoning controls, full context length, prompt caching, speculative decoding, streaming, log probabilities, and OpenAI Chat, Responses, and Anthropic API compatibility.
  • Identify capability and conformance gaps from testing and telemetry, prioritize them, and drive them to closure with engineering.
  • Work with Quality Assurance and Solutions to set model-launch acceptance criteria and evaluation requirements.
  • Test new models using real prompts, agentic tasks, and feature checks to identify strengths, weaknesses, and gaps.
  • Identify models, capabilities, and use cases where SambaNova's speed and efficiency provide a competitive advantage, and support the assessment with evidence.
  • Build quick demos, prototypes, and product mock-ups, such as a small application, notebook, or user interface, to demonstrate model value to leadership, go-to-market teams, and customers; hand promising ideas to Solutions Engineering for productionization.
  • Use hands-on findings to inform the model stack rank, feature bar, and launch messaging.
  • Track open-weight model releases, architectures such as mixture of experts, multi-head latent attention, and grouped-query attention, and benchmark and usage signals from Artificial Analysis, OpenRouter, and Hugging Face; assess their significance.
  • Triage requests from customers, field teams, and the Model Feedback process, close the loop with requesters, and represent demand accurately in the model rank.
  • Compare SambaNova's models, speed, and features with GPU clouds and other inference providers, and translate findings into roadmap and messaging.
  • Improve the roadmap process and its data pipeline using Jira, internal platform application programming interfaces, and public signals, keeping it trustworthy, auditable, and automated.

Desired Qualifications

  • Product experience at an inference provider, model lab, AI cloud, or AI hardware company.
  • Hands-on familiarity with inference stacks such as vLLM, SGLang, and TensorRT-LLM, and with OpenAI- and Anthropic-compatible APIs.
  • Experience designing evaluations and benchmark methodologies for model quality and feature conformance.
  • A background as a machine learning or solutions engineer before moving into product.
  • Experience with enterprise, sovereign AI, or public-sector customers.
  • An active presence in the open-source model community.

About the Company

SambaNova Systems

Enterprise AI platform with hardware stack

Generating company summary.

Company size
201-500
Company stage
Series F
Total funding
$2.5B
Headquarters
Palo Alto, CA
Founded
2017

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