Software Engineer, Agentic AI Systems

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

Moveworks is seeking a Software Engineer for their Agentic AI Systems team, focusing on building and evolving AI agent systems. The role involves implementing frontier AI algorithms, productionizing them at scale, and enhancing products using LLMs and AI agents. Responsibilities include agent orchestration, sandboxed execution environments, latency optimization, and working with enterprise knowledge graphs and multimodal I/O.

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

  • Strong programming fundamentals and experience building software through coursework, projects, internships, and/or research
  • Ability to think and clearly about engineering problems and systems
  • Eagerness to learn, give and receive feedback, and hold yourself to a high standard of operational excellence
  • Readiness to hit the ground running in a Mac development environment, programming in Python, Golang, and/or Java
  • Desire to ship at a startup pace with a high degree of ownership
  • Attention to detail
  • Drive to ship product improvements with production-grade code (with guidance and iterative development)
  • Strong appetite for continuous incremental wins and completing challenging projects fast
  • High level of curiosity about engineering outside of immediate discipline and ongoing desire to learn and stay at the cutting edge of applied AI

Nice to have

  • Internship or research experience building production-quality software
  • Experience building with LLMs, particularly in iterating on prompts, on model selection, on cognitive architecture design, and on latency/correctness tradeoffs in a data-driven way
  • Hands-on experience with one or more stages of a machine learning problem-solving lifecycle, such as experiment setup, dataset curation, model training, offline evaluation and error analysis, deployment, and online evaluation
  • Experience in AI fairness, privacy, permission controls, safety, and/or 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

Other signals

  • AI agents
  • LLMs
  • orchestration
  • enterprise knowledge graphs
  • multimodal I/O