Senior Software Engineer I, Agentic AI Systems

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

Senior Software Engineer on the Agentic AI Systems team, focused on building and evolving capable AI agent systems. The role involves implementing frontier AI algorithms, productionizing them at scale, and enhancing products with LLMs and AI agents. Key areas include agent orchestration, execution environments, optimization, memory, self-reflection, knowledge graphs, and multimodal I/O.

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 help 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. Contribute to roadmap and help drive the evolution of engineering areas of growing scope and impact to create lasting value for all our customers
  5. Mentor other engineers on the team in how to build, maintain, and evolve great software

Skills

Required

  • 2+ 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

  • production systems
  • agent orchestration
  • 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 at scale