Machine Learning Engineer III Senior Machine Learning Engineer AI Platform

Your work days are brighter here.

Were obsessed with making hard work pay off for our people our customers and the world around us. As a Fortune 500 company and a leading AI platform for managing people money and agents were shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join youll feel it. Not just in the products we build but in how we show up for each other. Our culture is rooted in integrity empathy and shared enthusiasm. Were in this together tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether youre building smarter solutions supporting customers or creating a space where everyone belongs youll do meaningful work with Workmates whove got your return well give you the trust to take risks the tools to grow the skills to develop and the support of a company invested in you for the long haul. So if you want to inspire a brighter work day for everyone including yourself youve found a match in Workday and we hope to be a match for you too.

About the Team

About the Role

We are looking for creative results-focused ML Engineers and Senior ML Engineers to help us build the next generation of AI-first products.

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 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.

In this role you would:

• 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 processfrom exploration and prompt engineering to scalable production deploymentensuring 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: 0.5 years of proven track record of building and evaluating NLP and 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: 0.5 years expert-level Python experience with a focus on modular library design asynchronous patterns and scalable system architecture (state management/error handling) for non-deterministic AI outputs.

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: 1 years of proven track record of building and evaluating NLP and 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: 1 years of expert-level Python experience with a focus on modular library design asynchronous patterns and scalable system architecture (state management/error handling) for non-deterministic AI outputs.

Other Qualifications

• Academic Foundation: Advanced degree (Masters 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).

• 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).

• Collaborative Leadership: Demonstrated ability to lead cross-functional teams mentor junior engineers and solve ambiguous problems with high autonomy.

Workday Pay Transparency Statement

The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidates compensation offer will be based on multiple factors including but not limited to geography experience skills job duties and business need among other things. For more information regarding Workdays comprehensive benefits please click here.

Additional Considerations:

Our Approach to Flexible Work

With Flex Work were combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections maintain a strong community and do their best work. We know that flexibility can take shape in many ways so rather than a number of required days in-office each week we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers prospects and partners (depending on role). This means youll have the freedom to create a flexible schedule that caters to your business team and personal needs while being intentional to make the most of time spent together. Those in our remote home office roles also have the opportunity to come together in our offices for important moments that matter.

Pursuant to applicable Fair Chance law Workday will consider for employment qualified applicants with arrest and conviction records.

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

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