Machine Learning Engineer, Workday
$136.2-240k
Salary is dependent on location
More information about location
Enterprise AI platform for managing HR and finance
Open for applications
Enterprise AI platform for managing HR and finance
1001+ employees
Open for applications
$136.2-240k
Salary is dependent on location
More information about location
1001+ employees
Company mission
To leverage the power of AI to elevate humans and supercharge work, moving business forever forward.
Role
Who you are
- We’re looking for highly creative, results-focused, and deeply skilled Machine Learning Engineers/scientists to work with us on a range of these challenges
- We are seeking pragmatic ML Engineers to drive the applied research, deployment, and optimization of our Agentic AI, Search, and Semantic Parsing products
- 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 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: 2+ years of Python experience with a focus on modular library design, asynchronous patterns, and scalable system architecture (state management/error handling) for non-deterministic AI outputs
- 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)
- 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
What the job involves
- This is a very exciting opening in the AI Platform team in our Information Retrieval and Agent Evaluation team
- We believe if you do what you love, you’ll love what you do
- There’s a lot to love at Workday
- We are part of a global, high-growth technology company and our team has the opportunity to develop the next generation of Workday’s groundbreaking collaborative products supporting a customer base of more than 31 million strong
- Over 65% of the Fortune 500 are Workday customers
- The Agent Evaluation Platform project is the "Ground Truth" engine for Workday’s AI transformation and we have an ambitious roadmap
- As Workday infuses AI Agents into every facet of our enterprise suite, our team provides the critical infrastructure and algorithms needed to prove they work—and make them better
- We build the platform that enables agent engineering teams to be empowered with rigorous, data-driven optimization, evaluation and validation of their agents
- The AI Platform Information Retrieval products are at the heart of Workday’s intelligence layer
- We bridge the gap between human language, search, and enterprise data, including reasoning over knowledge
- Our products utilize advanced semantic search to navigate Workday’s massive data model, as well as turning natural language questions into precise SQL and Python executions
- Workday’s AI Platform organization is bringing “AI first” products to life at every step of the Workday product offering
- 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
- 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
Our take
Workday is a cloud-based software vendor that provides management of workforces and finances. The company uses a single database for workforce management and financial management to offer integration and optimize business intelligence across companies. It has added to the packages and tools it offers over the years, providing analytic options that allow companies to combine third-party data with information collected by Workday.
The company has also grown through acquisitions and partnerships to broaden its capabilities.. Most recently, in 2025 it purchases Sana, an AI-driven enterprise knowledge and learning platform, to create a "new front door for work". This acquisition allows Workday to integrate Sana's AI-powered search, agents, and learning capabilities with its own data and workflows, allowing employees to access information, automate tasks, and complete their day entirely within Workday.
Through a combination of strategic acquisitions and in-house development, WOrkday has built a comprehensive suite of HR, financial, and enterprise tools. By focusing on innovation and AI-driven workflows, the company continues to push productivity and decision-making for organizations worldwide. Building on this track record, Workday is very much positioned for even more growth, reinforcing its status as a leader in shaping the future of work.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
16% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Oct 2011
$85m
SERIES F
Apr 2009
$75m
SERIES E
Total funding: $215.3m
Company HQ
Pleasanton, CA
Leadership
Aneel Bhusri
(Co-Founder & Executive Chair)Annel began his career at Morgan Stanley as a Corporate Finance Analyst. He then spent nearly 6 years at PeopleSoft as a Senior VP before co-founding Workday in 2005, where he has served as the CEO ever since.
Carl Eschenbach
(CEO)Carl joined Workday in 2022 as Co-CEO, becoming CEO in 2024. He has also been a Partner at Sequoia Capital since 2016. Previously, he spent 14 years at VMware as President and COO and 3 years at Inktomi Corporation as the VP of North America Sales.
Peter Bailis
(CTO)Prior to joining Workday in 2025, People was a VP of Engineering at Google. He also previously founded Sisu Data, where he served as the CEO for over 5 years.
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