Senior Software Engineer Ii, Agentic AI Systems

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

Senior Software Engineer II on the Agentic AI Systems team focused on building and evolving capable AI agent systems, productionizing frontier AI algorithms, and enhancing products with LLMs and AI agents. The role involves agent orchestration, execution environments, memory, self-reflection, knowledge graphs, and multimodal I/O, aiming to advance the frontier of work entrusted to agents.

What you'd actually do

  1. 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. Implement frontier AI algorithms and architectures and productionize them at scale
  3. Use the latest advances in machine learning, LLMs, and AI agents to enhance our products and create delightful user experiences
  4. Influence roadmap and drive the evolution of engineering areas of increasing scope and impact to create lasting value for all our customers
  5. Hone your craft in writing robust, extensible, readable, and performant code

Skills

Required

  • 4+ years experience designing, building and improving production systems, ideally at scale
  • Ability to think and communicate clearly about complex engineering problems and systems
  • Comfort giving and receiving feedback, and in holding yourself and your coworkers accountable 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
  • 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

What the JD emphasized

  • cutting edge of AI agents
  • advance the frontier of work that can be entrusted to agents
  • 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

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