Senior ML Engineer

About

We are seeking pragmatic ML and Senior ML Engineers to drive the applied research, deployment, and optimization of our Agentic AI, Search, and Semantic Parsing products. In this role, you will bridge the gap between deep research and production, embedding cutting-edge agents directly into the Workday ecosystem. Leveraging our vast computing power and exclusive datasets, you will solve complex technical challenges to deliver transformative value to millions of users. If you are ready to apply creative problem-solving to global-scale ML systems, we want to hear from you.

Responsibilities

• Architect Agentic AI: Design and deploy sophisticated reasoning, planning, and swarm agents that interact seamlessly with enterprise data and support continuous, life-long learning.
• Drive Meta-ML & Optimization: Develop algorithms for automated node-level optimization within agent graphs, identifying the best LLM and prompt configurations for every workflow step. Build recommender systems for engineering teams to drive optimal evaluation for their agents.
• Advance Information Retrieval: Build hybrid, agentic search systems and semantic parsing products (Text-to-SQL/Python) utilizing vector search, reasoning, and fine-tuning for structured output.
• Scale Evaluation & Observability: Engineer cloud-based pipelines (Kubeflow) and A/B testing frameworks for rigorous offline/online evaluation, failure attribution, and safety monitoring.
• Lead the ML Lifecycle: Own the end-to-end MLOps process—from exploration and prompt engineering to scalable production deployment—ensuring high-quality, reliable performance.
• Define Strategic Roadmaps: Independently identify ML opportunities, propose high-impact solutions to leadership, and integrate industry best practices across the organization.
• Collaborate with Autonomy: Work cross-functionally with PMs and Engineers to deliver "AI-first" products, enjoying full ownership of your work within a supportive, growth-oriented culture.

About You

Basic Qualifications (MLE III)

• Deep Technical ML Capability: 3+ years of experience researching, developing and deploying production-grade ML systems, including expertise in deep learning, NLP, Information Retrieval, and recommender systems using frameworks like PyTorch or TensorFlow.
• Generative AI & Agentic Systems: Proven track record of building and evaluating LLM-powered products, including expertise in RAG architectures, agentic frameworks (e.g., LangChain/LangGraph), and long-context LLM applications (e.g., Text-to-SQL).
• Engineering Excellence: Expert-level Python skills with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI outputs.
• Production MLOps: Hands-on experience with the full ML lifecycle, including model fine-tuning (PEFT), evaluation frameworks (e.g., DeepEval/RAGAS), and cloud-native deployment (Docker/K8s, AWS/GCP).

Basic Qualifications (Senior MLE)

• Deep Technical ML Leadership: 6+ years of experience researching, developing and deploying production-grade ML systems, including expertise in deep learning, NLP, Information Retrieval, and recommender systems using frameworks like PyTorch or TensorFlow.
• Generative AI & Agentic Systems: Proven track record of building and evaluating LLM-powered products, including expertise in RAG architectures, agentic frameworks (e.g., LangChain/LangGraph), and long-context LLM applications (e.g., Text-to-SQL).
• Engineering Excellence: Expert-level Python skills with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI outputs.
• Production MLOps: Hands-on experience with the full ML lifecycle, including model fine-tuning (PEFT), evaluation frameworks (e.g., DeepEval/RAGAS), and cloud-native deployment (Docker/K8s, AWS/GCP).

Other Qualifications

• Academic Foundation: Advanced degree (Master’s or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications.
• Optimization & Advanced Techniques: Proficiency in techniques like DSPy, Reinforcement Learning, imitation learning, graph neural networks, multi-modal models, and large-scale data processing (PySpark, SQL).
• Experimental Rigor: A "test-everything" mindset with experience in A/B testing, Knowledge Graphs, and "Golden Dataset" curation for model benchmarking.
• Data Pipelines: Proficiency in large-scale data processing (PySpark, SQL).
• Collaborative Leadership: Demonstrated ability to lead cross-functional teams, mentor junior engineers, and solve ambiguous problems with high autonomy.