Software Engineer, Agentic AI Systems

Moveworks Moveworks · Enterprise · Mountain View, CA +1 · Machine Learning

Software Engineer role focused on building and evolving AI agent systems, including agent orchestration, code execution, latency optimization, and LLM self-reflection, for the Moveworks AI Assistant platform. The role involves implementing frontier AI algorithms, productionizing them at scale, and enhancing products using the latest advances in ML, LLMs, and AI agents.

What you'd actually do

  1. Take on exciting engineering challenges (see areas listed above) to build and evolve capable AI agent systems that are reliable in every sense of the word
  2. Implement frontier AI algorithms and architectures and help productionize them at scale, with support from senior engineers
  3. Use the latest advances in machine learning, LLMs, and AI agents to enhance our products and create delightful user experiences
  4. Execute on projects to create lasting value for all our customers
  5. Hone your craft in writing robust, extensible, readable, and performant code

Skills

Required

  • Python
  • Golang
  • Java
  • software engineering fundamentals
  • building software through coursework, projects, internships, and/or research
  • ability to think and communicate clearly about engineering problems and systems
  • eagerness to learn, give and receive feedback
  • attention to detail

Nice to have

  • building with LLMs
  • iterating on prompts
  • model selection
  • cognitive architecture design
  • latency/correctness tradeoffs in a data-driven way
  • experiment setup
  • dataset curation
  • model training
  • offline evaluation and error analysis
  • deployment
  • online evaluation
  • AI fairness
  • privacy
  • permission controls
  • safety
  • security

What the JD emphasized

  • cutting edge of AI agents
  • advance the frontier of work that can be entrusted to agents to perform reliably at scale
  • agent orchestration
  • sandboxed file systems and code execution
  • latency optimization
  • agent memory
  • LLM self-reflection and improvement
  • execution environment simulation
  • enterprise knowledge graphs
  • multimodal I/O
  • productionize them at scale
  • enterprise AI product
  • building software through coursework, projects, internships, and/or research
  • ship at a startup pace with a high degree of ownership
  • production-grade code
  • applied AI

Other signals

  • AI agents
  • LLMs
  • orchestration
  • productionize at scale