Staff Machine Learning Engineer, Agentic AI Systems - Moveworks

ServiceNow ServiceNow · Enterprise · Mountain View, CA +1 · Engineering

Staff Machine Learning Engineer focused on expanding agentic AI capabilities, NLU, and generative AI for the Moveworks platform, which is now part of ServiceNow. The role involves applying compound AI system engineering, fine-tuning LLMs for tool use and enterprise reasoning, developing agent cognitive architectures, and ensuring responsible AI practices. It requires a strong understanding of ML fundamentals, LLMs, and productionizing solutions at scale, with a focus on both model performance and end-to-end system performance.

What you'd actually do

  1. Apply software engineering, machine learning, and compound AI system engineering to create lasting value for all our customers
  2. Take on exciting and difficult challenges in conversational agent domains, such as agent cognitive architecture iteration, multimodal agents, multilingual agents, conversational memory management, reasoning strategies (eg Tree of Thoughts / Graph of Thoughts), fine-tuning LLMs for tool use and enterprise reasoning (including preference alignment with RLHF/RLAIF/DPO), agent evaluation, active learning of exemplars for few-shot text classification, abstractive summarization, and grounding & verifiability for generated text
  3. Push the envelope of Moveworks commitments to responsible AI, expanding our infrastructure for ensuring models work equally well for all people, red-teaming models to ensure they behave safely and as intended, and keeping our ML at the cutting edge of data privacy and security
  4. Use your knowledge of machine learning fundamentals and LLMs to design new algorithms and architectures, evaluate them with small scale experiments and productionize your solutions at scale
  5. Research and develop innovative, scalable and dynamic solutions to hard problems

Skills

Required

  • software engineering
  • machine learning
  • LLMs
  • conversational agent domains
  • agent cognitive architecture
  • multimodal agents
  • multilingual agents
  • conversational memory management
  • reasoning strategies
  • fine-tuning LLMs
  • tool use
  • enterprise reasoning
  • preference alignment
  • RLHF/RLAIF/DPO
  • agent evaluation
  • active learning
  • few-shot text classification
  • abstractive summarization
  • grounding & verifiability
  • responsible AI
  • red-teaming models
  • data privacy and security
  • ML fundamentals
  • design new algorithms and architectures
  • evaluate them with small scale experiments
  • productionize your solutions at scale
  • innovative, scalable and dynamic solutions

Nice to have

  • multimodal foundation models
  • hybrid vector databases
  • world-class annotation team
  • Tree of Thoughts
  • Graph of Thoughts
  • latest ML research
  • open-source code

What the JD emphasized

  • agentic AI
  • compound AI system engineering
  • fine-tuning LLMs for tool use
  • enterprise reasoning
  • grounding & verifiability
  • productionize your solutions at scale

Other signals

  • agentic AI platform
  • LLMs
  • compound AI systems
  • fine-tuning LLMs for tool use
  • enterprise reasoning
  • conversational memory management
  • reasoning strategies
  • grounding & verifiability