Sr AI Engineer, AI Platform

Samsara Samsara · Enterprise · CA · Remote · Revenue Operations

Sr. AI Engineer II on the Revenue Operations AI & Data Team, focused on building production-grade generative AI applications to transform sales operations. The role involves developing AI-powered copilots, generating outreach at scale, and enabling voice/chat interfaces, working across the stack from model experimentation to app development and infrastructure.

What you'd actually do

  1. Build GenAI tools that help sellers find and connect with the right customers faster.
  2. Develop AI-powered copilots that surface CRM insights and sales knowledge in real time.
  3. Launch AI features that improve efficiency and personalization across the sales funnel.
  4. Work hands-on across the stack: from model experimentation to app development and infrastructure.
  5. Collaborate with top-tier engineers, data scientists, and sales operators in a fast-moving environment.

Skills

Required

  • 5+ years of industry experience, with a focus on software engineering, applied machine learning, or AI product development.
  • Demonstrated success building and deploying production-ready AI/ML applications, particularly in generative AI (e.g., LLMs, prompt engineering, embeddings, RAG systems).
  • Expert in Python and GenAI frameworks (e.g., LangChain, OpenAI SDK, MCP, etc.)
  • Experience developing backend systems and APIs, and working closely with product and design to ship customer-facing features.
  • Strong product sense and ability to work in fast-paced, cross-functional environments.

Nice to have

  • Experience working on AI products in a sales, go-to-market, or revenue operations context.
  • Familiarity with enterprise tools like Salesforce, Gong, Outreach, or similar CRM/enablement platforms.
  • Experience integrating with vector databases, retrieval systems, or streaming voice/chat pipelines.
  • A track record of leading major technical initiatives or mentoring engineers in high-growth teams.
  • Exposure to infrastructure considerations such as cost optimization, model evaluation, observability, and latency management.

What the JD emphasized

  • production-ready AI/ML applications
  • generative AI
  • LLMs
  • prompt engineering
  • embeddings
  • RAG systems
  • Python
  • GenAI frameworks
  • LangChain
  • OpenAI SDK
  • MCP
  • backend systems
  • APIs
  • customer-facing features

Other signals

  • building production-grade generative AI applications
  • powering sales operations through AI
  • generating high-quality outreach at scale
  • building copilots for sellers
  • enabling voice and chat interfaces