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About This Role
Your work days are brighter here.
We’re 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, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll 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. We’re 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 you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll 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, you’ve found a match in Workday, and we hope to be a match for you too.
About the Team
The Data Cloud organization is at the forefront of Workday’s next major wave of innovation, driven by a bold mandate to build the industry’s first true Platform for Agents. We are a high-energy, ambitious team that balances Workday's deep foundational expertise with fresh, forward-thinking perspectives to accelerate our pace of delivery and unlock massive new commercial value. By leveraging Workday’s unique business context, trusted data, and identity guardrails, we are building the critical engine that will power the future of AI and agentic workflows across the enterprise. As a team, we value organizational clarity, deep cross-functional collaboration, and rapid execution, working together to evolve our architecture and aggressively scale our impact in the market.
Join the Data Cloud organization, where we are building the intelligent foundation for Workday's next generation of AI Agents. The Context and Semantics Engineering team is the brain trust responsible for making our AI "enterprise-aware." We build the semantic layers, knowledge graphs, and dynamic context engines that ground generative AI in real-world HR and Finance data. By bridging the gap between raw enterprise data and large language models (LLMs), our team ensures that Workday’s autonomous agents can reason accurately, securely, and with deep contextual awareness at a global scale. About the Role
As the Senior Director of Context and Semantics Engineering, you will be a key engineering executive driving the vision, architecture, and delivery of the intelligence layer powering Workday’s AI Agents. You will lead and scale an engineering organization to build highly scalable semantic engines, advanced RAG (Retrieval-Augmented Generation) pipelines, vector search infrastructure, and dynamic context-management systems.
Sitting at the critical intersection of data architecture and applied AI, you will ensure that our foundational models seamlessly understand and reason over complex, highly regulated enterprise workflows. This is a high-visibility, high-impact role requiring a visionary leader who can scale organizations, navigate technical ambiguity, and deliver production-grade AI infrastructure that sets the industry standard for Enterprise AI.
Responsibilities:
- Organizational Leadership: Build, scale, and mentor a world-class engineering organization dedicated to AI context, semantic architecture, and data orchestration.
- Strategic Technical Vision: Define and execute the technical roadmap for Workday’s AI semantic layer, encompassing vector databases, knowledge graphs, enterprise ontologies, and semantic search capabilities.
- AI & RAG Infrastructure: Lead the development of enterprise-grade RAG pipelines, ensuring LLMs are grounded in real-time, accurate, and tenant-isolated HR and Finance data to eliminate hallucinations and drive agentic reasoning.
- Cross-Functional Executive Partnership: Collaborate tightly with VP/C-level stakeholders across AI/ML Engineering, Product Management, and Platform Architecture to seamlessly integrate context engines into the broader AI Agent Factory and Sana experiences.
- Context & Memory Management: Drive the engineering design for agentic memory, state management, and conversational context, allowing our AI agents to seamlessly handle multi-turn, complex user workflows.
- Responsible & Governed AI: Enforce rigorous technical standards for data privacy, RBAC (Role-Based Access Control), and compliance, ensuring that context fed to AI models strictly adheres to enterprise security boundaries.
- Innovation & Scale: Evaluate and integrate cutting-edge AI data technologies and orchestration frameworks (e.g., advanced embedding models, GraphRAG, LlamaIndex, LangChain) to future-proof Workday's AI platform at massive scale.
About You
Basic Qualifications:
- 10+ years of progressive software engineering experience with a strong background in distributed systems, search architectures, data platforms, or AI infrastructure.
- 5+ years of senior engineering leadership experience (Director level or above), with a proven track record of managing managers, scaling organizations (50+ engineers), and delivering mission-critical enterprise systems.
- 3+ years of direct experience leading teams that build AI/ML infrastructure, semantic search engines, LLM orchestration layers, vector databases, or Knowledge Graphs.
Other Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field (Ph.D. is a plus).
- Deep architectural understanding of how to bridge structured enterprise data with unstructured AI models (RAG architectures, embedding generation, chunking strategies, and semantic routing).
- Experience building and scaling Semantic Layers, Ontologies, or enterprise-scale Search/Retrieval systems (e.g., Elasticsearch, Pinecone, Milvus, Neo4j).
- Strong executive presence and communication skills, with the ability to translate complex AI and data concepts into clear business value for non-technical stakeholders.
- Product-first mindset with a relentless focus on delivering high-reliability, low-latency infrastructure that powers real-time AI user experiences.
- Experience operating within highly regulated data environments (Finance/HR) and implementing AI guardrails for enterprise-grade security.
- Experience with the Workday platform and architecture is a strong plus.
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 candidate’s 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 Workday’s comprehensive benefits, please click here .
Primary Location: USA.CA.Pleasanton
Primary Location Base Pay Range: $281,600 USD - $422,400 USD
Additional US Location(s) Base Pay Range: $231,800 USD - $422,400 USD
Our Approach to Flexible Work
With Flex Work, we’re 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 you'll 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.
Workday is committed to providing reasonable accommodations for qualified individuals during our application process, in order to perform one or more essential functions of their job, as well as regarding the use of AI tools for employment decision-making to any degree. Please see below for more details including how to request an accommodation as a qualified veteran, due to a disability or for religious reasons, or as otherwise provided under applicable law.
Workday prohibits taking adverse action against any candidate or employee for reporting a possible violation of this policy, requesting one or more work accommodations, exercising a privacy right, or cooperating in an investigation in accordance with applicable law. Any employee who retaliates against a candidate or employee for doing so may be subject to disciplinary action, up to and including termination of employment, to the fullest extent allowable under applicable law.
If you require a reasonable accommodation, you may email [email protected] , as far in advance as possible.
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Salary Context
This $281K-$422K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Workday, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($352K) sits 66% above the category median. Disclosed range: $281K to $422K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Workday AI Hiring
Workday has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Agent Developer. Positions span Pleasanton, CA, US, Reston, VA, US, Pleasanton, CA. Compensation range: $237K - $422K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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