Senior Machine Learning Engineer Ii, Nlu & Agentic AI

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

Senior Machine Learning Engineer II focused on NLU and Agentic AI at Moveworks. The role involves applying software engineering and ML to create value for customers by developing conversational agents, multimodal agents, and improving generative AI capabilities. Responsibilities include fine-tuning LLMs for tool use, agent evaluation, responsible AI, designing new algorithms, and productionizing solutions. Requires Python/Golang, ML fundamentals, and LLM knowledge.

What you'd actually do

  1. Apply software engineering, machine learning, and [compound AI system engineering](https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/) 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](https://www.moveworks.com/insights/moveworks-enterprise-llm-benchmark-evaluates-large-language-models-for-business-applications) (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](https://www.moveworks.com/insights/what-is-grounding-ai).
  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

  • Python
  • Golang
  • machine learning fundamentals
  • LLM knowledge
  • deep learning architectures and algorithms
  • leading large language models
  • model evaluation fundamentals
  • text generation
  • text classification
  • non-uniform sampling regimes
  • software engineering

Nice to have

  • Experience productionizing ML models at scale
  • AI fairness
  • privacy
  • permission controls
  • safety
  • security
  • PyTorch
  • LightGBM
  • HuggingFace Transformers
  • PEFT
  • SpaCy 3
  • iterating on prompts for large language models in a data-driven way

What the JD emphasized

  • production-quality, fully unit-tested code
  • rigorously-evaluated updates
  • production-grade code

Other signals

  • building agentic AI systems
  • improving generative and conversational AI capabilities
  • fine-tune, evaluate, and serve models in production
  • state-of-the-art AI performance in production
  • design and evolve great compound AI systems