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About the Role Moveworks is seeking an experienced software engineer with machine learning expertise to expand our NLU and agentic AI capabilities. You will work on a production‑grade, data‑driven ML platform to improve conversational AI across enterprise workflows, focusing on reliability, latency, and end‑to‑end user experience. What You'll Do

• Apply software engineering, machine learning, and compound AI system engineering to create lasting value for all our customers
• Tackle challenging conversational agent domains, such as agent cognitive architecture iteration, multimodal agents, multilingual agents, conversational memory management, reasoning strategies (e.g. 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
• 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
• 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
• Research and develop innovative, scalable and dynamic solutions to hard problems
• Use the latest advances in machine learning and LLMs to enhance our products and create delightful user experiences
• Spend time weekly reading, discussing, and potentially building models out of the latest ML research and open-source code What We're Looking For
• Drive to ship product improvements with production-quality, fully unit-tested code and rigorously-evaluated updates to models, prompts, or other tunable system components
• Ability to solve problems end-to-end with machine learning
• Solid grasp of model evaluation fundamentals, especially for text generation, text classification, and non-uniform sampling regimes
• Attention to detail and high standard of data quality Nice to Have
• Experience with RLHF/RLAIF/DPO and fine-tuning LLMs for tool use and enterprise reasoning
• Interest in responsible AI, privacy, and security considerations Compensation & Benefits
• Not disclosed