Staff Software Engineer, Agentic AI Systems

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

Staff Software Engineer on the Agent Lab team focused on advancing AI agents for enterprise use, leading the evolution of the Moveworks AI Assistant platform in areas like agent orchestration, execution environments, memory, and multimodal I/O. The role involves defining and delivering distributed AI systems, setting technical direction, and collaborating with ML engineers to build world-class AI systems.

What you'd actually do

  1. Lead and take on exciting and difficult engineering challenges (see areas listed above) to build and evolve capable AI agent systems that are reliable in every sense of the word
  2. Define, drive, and deliver frontier AI distributed systems and ensure they are productionized at scale, or use the latest advances in machine learning, LLMs, and AI agents to enhance our products and create delightful user experiences
  3. Set technical direction, influence roadmap, and drive the evolution of engineering areas of broad scope and impact to create lasting value for all our customers
  4. Set a high bar for writing robust, extensible, readable, and performant code, and raise the engineering standard for the team
  5. Partner with senior subject matter experts across the company including in machine learning, security, product, user experience, and customer success, to align teams and build the best enterprise AI products the world has ever seen

Skills

Required

  • 6+ years experience designing, building, and improving production systems
  • Demonstrated ability to lead technical design and execution across multiple projects and stakeholders
  • Ability to think and communicate clearly about complex engineering problems and systems, and to drive alignment across teams
  • Comfort giving and receiving feedback, and in holding yourself and your coworkers accountable to a high standard of operational excellence
  • Mac development environment
  • Python
  • Golang
  • Java
  • Desire to ship at a startup pace with a high degree of ownership
  • Strong attention to detail
  • track record of shipping high-quality, production-grade systems
  • 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

  • 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 driving 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
  • Experience designing and operating large-scale, high-reliability systems with strong observability, SLOs, and on-call excellence

What the JD emphasized

  • production systems
  • AI agents
  • agent orchestration
  • execution environment simulation
  • enterprise knowledge graphs
  • multimodal I/O
  • LLM self-reflection
  • productionized at scale
  • enterprise AI products
  • production systems
  • production-grade systems
  • applied AI

Other signals

  • agent orchestration
  • LLM self-reflection
  • execution environment simulation
  • enterprise knowledge graphs
  • multimodal I/O