Workday’s Agent Factory is building production-grade intelligent agents that are embedded into Workday’s platform, not lab experiments. You’ll join small, senior, cross-functional AI teams to design, implement, and integrate core capabilities for agents used by millions of people every day. The work emphasizes end-to-end ownership, tight collaboration across product and engineering, and responsible delivery of AI that can operate over sensitive HR and financial data at global scale. This role is hybrid in Pleasanton, CA.
Responsibilities
- Lead end-to-end system design, implementation, and product integration for a core domain of Workday’s next generation of intelligent agents.
- Connect AI capability to the product through intelligence orchestration and product delivery.
- Develop and integrate foundational models safely and reliably into functional, production-grade software.
- Work hands-on on design, experimentation, and orchestration of complex agentic workflows.
- Translate advanced AI capabilities into scalable business value.
- Implement strict guardrails for data privacy, predictability, and explainability for agents that interact with sensitive enterprise HR and financial data.
- Balance domain-level architecture with rigorous execution to address critical constraints such as latency, cost, and reliability.
Requirements
- 8+ years of professional software engineering experience with strong expertise in backend architecture, distributed systems, and API design, plus one of the following:
- 1+ years dedicated to building production-grade LLM/agentic systems
- or 5+ years in Machine Learning Engineering or AI application development, including 2+ years shipping LLM-backed products
- 2+ years hands-on experience integrating large models (LLMs, Foundation Models) and modern AI APIs into user-facing enterprise products.
- 1+ years experience designing and scaling AI orchestration architectures, including multi-agent frameworks, routing layers, or advanced RAG pipelines.
- 4+ years experience optimizing application performance, including constraints like API latency and user interaction design, with 1+ years focused on modern LLM constraints (token management, cost optimization, and context-window efficiency).
- 4+ years experience leveraging cloud computing platforms (AWS, GCP) to deploy responsive, scalable systems.
- Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field.
- Strong understanding of governance, guardrails, security layers, and evaluation mechanisms for autonomous agents over sensitive enterprise data.
- Proven ability to technically lead engineering workstreams within a pod, take ownership across the development lifecycle, and mentor junior-to-mid level engineers.
- Deep focus on business value and user experience, applying deep learning and large models to solve practical end-user challenges.
- Proven capability to architect robust application layers around AI models, enabling reusable patterns for predictability, error handling, and seamless UX integration.
- Experience prototyping and benchmarking model outputs against product requirements, including automated evaluation metrics such as retrieval quality and agentic behavior.
- Highly autonomous builder who can break open-ended goals into concrete, scalable engineering plans.
Technologies
AWS, GCP, LLMs, Foundation Models, multi-agent frameworks, routing layers, RAG pipelines, AI APIs, cloud computing platforms
Compensation and Pay Transparency
Annualized base salary ranges:
- Primary location (Pleasanton, CA): $208,000 USD - $312,000 USD
- Additional US locations: $176,000 USD - $312,000 USD
This role may also be eligible for the Workday Bonus Plan or a role-specific commission/bonus, and includes annual refresh stock grants. Pay ranges vary by work location, and offers are based on multiple factors including geography, experience, skills, job duties, and business need.
Work Model (Flex Work)
- At least 50% of time each quarter in the office or in the field with customers, prospects, and partners (depending on role).
- Remote “home office” roles may have opportunities to come together in Workday offices for important moments.
Equal Opportunity, Privacy, and Security
- Workday provides reasonable accommodations during the application process and offers accommodations related to the use of AI tools for employment decision-making.
- Workday prohibits adverse action for reporting policy violations, requesting accommodations, exercising privacy rights, or cooperating in investigations.
- Workday considers qualified applicants with arrest and conviction records in accordance with applicable Fair Chance Law.
- Workday will not ask candidates to apply through non-Workday Careers websites or to pay recruiting fees or pay for consulting/coaching to apply.
If you need a reasonable accommodation, email accommodations@workday.com as far in advance as possible.
Note: If you’re being referred to a Workday role, ask your connection about the Employee Referral process.
