Interested in this AI Product Manager role at Workday?
Apply Now →Skills & Technologies
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
Your work matters here. At Workday Government, we focus on outcomes that serve a larger mission. Our work supports U.S. federal agencies as they modernize and transform the full employee lifecycle experience and finance operations—so they can operate with greater clarity, accountability, and trust. As a Fortune 500 company and a proven enterprise cloud platform, Workday brings modern technology, responsible AI, and secure infrastructure to some of the most complex environments in the world. The work isn’t theoretical. It’s operational. It’s high-impact. And it demands rigor, integrity, and long-term thinking.
From day one, you’ll be part of a team that values collaboration, follow-through, and doing the right thing—especially when the stakes are high. Our culture is grounded in integrity, respect, and shared responsibility. We challenge each other to think clearly, act thoughtfully, and build solutions that stand up to real-world demands. Here, curiosity is matched with accountability. Ambition is paired with trust. You’ll have the space to do your best work, the support to keep growing, and the backing of a company committed to long-term investment in both its people and the federal mission.
If you’re looking to apply your experience to meaningful, mission-driven work—alongside colleagues who take pride in building things that last—you’ll find that opportunity at Workday Government. About the Role
Employees may be required to be on site at client locations in the DC, MD, and VA (DMV) area
The Workday ML Runtime team is seeking an energetic and determined Software Engineer to design, implement, and deliver highly scalable features for our Machine Learning Runtime platform. As a member of this fast paced group you will have a unique and rewarding opportunity to shape and contribute towards microservices that power Workday Machine Learning features in production. You will partner with Data Scientists, ML Engineers, and other Software Engineers to create the technology that brings these features to life.
Key Responsibilities:
- Developing frameworks, automation, and tooling to foster a culture of efficiency and innovation.
- Apply technologies like Kubernetes, Docker, and Python to enhance developer scalability in creating innovative ML Runtime Inference applications.
- Implementation and operation of distributed systems and software development including the conception, specifying, designing, programming, documenting, testing, and bug fixing involved in creating and maintaining applications, frameworks, or other software components.
- Developing products and services that empower developers to streamline their interactions with the ML platform.
- Working with public clouds (such as IAAS, AWS, GCP) and applying capacity management principles.
- Deploying and orchestrating containers in production environments, including technologies like Containers, Kubernetes, Service Mesh, ArgoCD and related tools.
- Actively engage with Tech Leads and ML Engineers across teams to elaborate on requirements and drive technical solutions.
- Own and develop features from end to end including infrastructure as code.
- Research, evaluate, prototype and drive adoption of new ML tools with reliability and scale in mind
- Strong dedication to proactively addressing and resolving issues, automating processes, and empowering engineers to self-service their operational needs for improved productivity.
Availability for on-call support on a rotational basis. *
This role will support one or more direct or indirect contracts with the U.S. Federal Government which, due to federal government security requirements, mandates that all Workday personnel working on the contracts be United States citizens (naturalized or native).
About You
This role may require a security clearance at the TS/SCI w/CI Poly level. Applicants must have the ability to obtain and maintain a U.S. government issued security clearance. An active TS/SCI w/CI Poly is preferred.
Basic Qualifications (P4 Sr. SDE)
- 7+ years of professional experience in DevOps engineering, infrastructure automation, and CI/CD pipeline development.
- 7+ years of experience with Python programming and container orchestration platforms (e.g., Docker, Kubernetes).
Bachelor’s degree in Computer Science, STEM field, or equivalent practical experience. *
Other / Preferred Qualifications
- MLOps & Domain Experience: Hands-on experience designing, deploying, and scaling Machine Learning runtime platforms and inference pipelines in partnership with ML teams.
- Tooling & Infrastructure: Proficiency with Infrastructure as Code (e.g., Terraform), Git SCM workflows, and GitOps/CD engines (e.g., ArgoCD, Jenkins).
- Observability: Experience building end-to-end monitoring, metrics, and alerting pipelines using telemetry stacks like Grafana or Prometheus.
- Architecture & Code Quality: Strong understanding of distributed systems, SaaS microservices, Object-Oriented Design (OOD), and automated testing methodologies (unit, integration, e2e).
- Leadership & Operations: Proven experience leading technical initiatives and mentoring team members; willingness to participate in a rotating on-call schedule.
Basic Qualification (P3 SDE)
- 5+ years of professional DevOps experience, including infrastructure automation and CI/CD pipeline development.
- 5+ years of experience with Python programming and containerization technologies (e.g., Docker, Kubernetes).
Bachelor’s degree in Computer Science, STEM field, or equivalent practical experience. *
Other Qualifications:
- MLOps Experience: Hands-on experience deploying, monitoring, and scaling Machine Learning runtime environments or pipelines alongside ML teams.
- Tooling & Infrastructure: Experience with Infrastructure as Code (e.g., Terraform), Git workflows, and GitOps/CD engines (e.g., ArgoCD, Jenkins).
- Observability: Experience building monitoring, metrics, and alerting systems using telemetry stacks such as Grafana or Prometheus.
- Software Fundamentals: Solid understanding of Object-Oriented Design (OOD), distributed systems, and SaaS microservice architectures.
- Quality & Operations: Familiarity with automated testing frameworks (unit, integration, e2e) and willingness to participate in a rotating on-call schedule.
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.VA.Reston Primary Location Base Pay Range: $163,800 USD - $245,800 USD Additional US Location(s) Base Pay Range: $148,200 USD - $264,000 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.
Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!
At Workday, we value our candidates’ privacy and data security. Workday will never ask candidates to apply to jobs through websites that are not Workday Careers.
Please be aware of sites that may ask for you to input your data in connection with a job posting that appears to be from Workday but is not.
In addition, Workday will never ask candidates to pay a recruiting fee, or pay for consulting or coaching services, in order to apply for a job at Workday.
Salary Context
This $148K-$264K range is above the median for AI Product Manager roles in our dataset (median: $193K across 99 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Workday, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($206K) sits 6% below the category median. Disclosed range: $148K to $264K.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.