Machine Learning Engineer

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 a highly skilled and pragmatic Machine Learning Engineer to work with us on the applied research development deployment and optimization of advanced ML systems Search Information Retrieval (IR) and Semantic Parsing products. You will be a crucial driver in embedding cutting-edge AI agent technology directly into Workday products moving quickly from deep applied research to robust production features. You will use Workdays vast computing resources on rich exclusive datasets to deliver value that transforms the way our customers make decisions and run their businesses. We will challenge you to apply your best creative thinking analysis problem-solving and technical abilities to make an impact on thousands of enterprises and millions of users. Sound like your kind of challenge

In this role you would:

Advanced Information Retrieval: Build and refine hybrid search systems (Vector Keyword Agentic and Multi-step Reasoning) that navigate Workdays proprietary data objects with high precision and recall. Apply deep learning techniques to enhance recommender systems ranking models and information retrieval systems driving relevance and personalization across core Workday applications.

Semantic Parsing & Code Gen: Design and optimize LLM model and agent based products for Text-to-SQL and Text-to-Python focusing on schema grounding few-shot prompting and fine-tuning for structured output.

Evaluation Platform Development: Establish rigorous and scalable methods for evaluating Information Retrieval products AI agents LLMs and other ML model accuracy and performance including developing metrics for quality safety latency and user experience.

AI Agent Engineering: Design build and deploy sophisticated AI agents (e.g. orchestration agents planning and execution nodes tool selection agents autonomous workflow agents conversational interface agents) that interact seamlessly with enterprise data. Work on continuous learning for the agents.

Prompt Engineering & Optimization: Develop test and maintain advanced prompt engineering and prompt optimization strategies and guardrails to ensure LLM-powered features are accurate safe and reliable at scale.

Production and MLOps: Own the entire ML lifecycle ensuring high-quality scalable deployment monitoring and continuous improvement of all models and agents in production environments.

Own exploration design and execution of advanced ML models algorithms and frameworks that deliver value to our users.

Collaborate with other ML engineers software engineers product managers and across teams to deliver your products through Workday end user applications.

Be given autonomy and ownership over your work but with the support of the entire organization.

Keep abreast of the latest advancements in ML/AI Information Retrieval Agentic AI Generative AI NLP research techniques and tools.

Have extraordinary opportunities for career growth and learning in a fast-growing forward-looking company.

About You

Basic Qualifications

3 years of professional experience as a Machine Learning Engineer focusing on researching developing building training and deploying deep learning Information Retrieval NLP solutions and generative/agentic AI systems into production.

Expertise in prompt engineering prompt optimization and developing robust strategies for integrating LLMs into user-facing products.

Experience researching developing and deploying production-grade recommender systems information retrieval systems and/or ranking models e.g. vector databases embedding models and RAG (Retrieval-Augmented Generation) architectures.

Demonstrated experience in building and evaluating AI agents including familiarity with agent frameworks RAG architectures and agent evaluation frameworks.

Expertise in language model fine-tuning techniques (e.g. parameter-efficient fine-tuning domain adaptation) and building models with mid and large model architectures (e.g. BERT family as well as LLMs).

Proven theoretical and practical understanding of statistical analysis and machine learning algorithms natural language processing especially for supervised unsupervised and self-supervised methods.

Engineering Excellence: Expert-level Python skills including topics relating to multi-threading api design matrix processing runtime memory design and asynchronous call patterns. Expected to have experience with modern ML frameworks (PyTorch HuggingFace) and orchestration libraries (LangGraph Haystack or LlamaIndex or equivalent).

Systems Design: Strong understanding of how to build scalable Agent-in-the-loop systems including error handling and state management.

Take ownership for finding creative algorithmic and system design solutions that move projects work-streams and products forward. Have determination to turn ideas into reality and improve user experience.

Solid understanding and experience with MLOps scalability and cloud services (e.g. AWS GCP Azure) containerization technologies (e.g. Docker) Kubernetes and large-scale ML systems.

Other Qualifications

A relevant advanced degree (Masters or Ph.D.) in Computer Science Machine Learning AI Computer/Software engineering or a related quantitative programming field.

Experience with embeddings (text multi-modal and graph) vector databases search indices informational retrieval techniques and large-scale data ingestion pipelines.

Data Mastery: Proficiency in SQL and experience with large-scale data processing (Spark Pandas).

Schema Expertise: Familiarity with Knowledge Graphs or complex relational data modeling is a huge plus.

Experience with advanced techniques such as reinforcement learning imitation learning graph neural networks and multi-modal models.

Evaluation Obsession: You dont trust a model until youve built a statistically sound way to break it. Experience with A/B testing and Golden Dataset curation.

Standout leader and colleague strong communication skills with experience working across functions and teams and working on ambiguous problems.

Bonus points for machine learning related research publications.

Resilience to obstacles and the ability to lead the solving of problems independently.

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.

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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