Machine Learning Engineer, Workday
CA$128-192k
More information about location
Enterprise AI platform for managing HR and finance
Be an early applicant
Enterprise AI platform for managing HR and finance
1001+ employees
Be an early applicant
CA$128-192k
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
- 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
- Proven, hands-on experience building and launching Generative AI products powered by long context LLMs, specifically applied to structured data tasks e.g. Text to SQL
- 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 (Master’s 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 don't trust a model until you've 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
What the job involves
- 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 Workday’s 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?
- Advanced Information Retrieval: Build and refine hybrid search systems (Vector, Keyword, Agentic, and Multi-step Reasoning) that navigate Workday’s 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
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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