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 We are hiring Sr ML Engineers across two teams.
Admin Central is building the future of how administrators work across Workday - turning a fragmented patchwork of tools and disconnected AI into a single, unified experience. Within Admin Central, Workday implementations move from being driven by human effort to being powered by intelligence designed into the platform.
The Business Process Optimize (BPO) team powers the underlying platform infrastructure. You’ll build mission-critical distributed systems, high-availability usage pipelines, new agentic functionality, and event-driven data flows across global product regions to ensure enterprise billing integrity at scale. If you're excited to build from scratch, move fast, and transform how thousands of admins work every day, come join us! About the Role
Key Responsibilities
- ML Infrastructure & Pipeline Engineering: Architect, deploy, and maintain end-to-end Machine Learning pipelines - from distributed data ingestion and feature engineering to model training, evaluation, and production deployment.
- Agentic AI & Recommendation Engines: Design algorithms and LLM-driven recommendation features (e.g., IDD insights, next-best-action models) that optimize agentic workflows and tool usage.
- Low-Latency Inference & Scaling: Optimize real-time model inference and scoring microservices to ensure sub-second response times across high-concurrency multi-tenant environments.
- LLM & Agentic Observability: Integrate telemetry and tracing (OpenTelemetry, LangChain, LangSmith) into ML pipelines to track model performance, prompt/response drift, and execution efficiency.
- Data & Feature Store Architecture: Collaborate with core backend engineers to leverage big data platforms (ClickHouse, Apache Iceberg, Spark) for high-dimensional feature retrieval and analytics.
- Cross-Functional Leadership: Partner closely with Product Managers, Data Engineering, SRE, and Legal/Compliance to translate complex business and governance requirements into scalable ML solutions.
About You
Basic Qualifications
- 8+ years of experience in machine learning development, researching, developing, and implementing advanced machine learning models and techniques, with at least 4+ years focused explicitly on building, deploying, and operating production Machine Learning systems at scale.
- 5+ years of experience and proficiency in deep learning, natural language processing (NLP), and GenAI/LLM orchestration, utilizing frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, LangChain, and LlamaIndex, alongside proficiency in Java, Go, Scala, or C++ for high-performance backend systems.
Other Qualifications
- Data Processing & Pipelines: Strong ability in distributed processing (Spark, Flink, Kafka) and high-throughput columnar/vector storage (ClickHouse, Pinecone, Milvus) to design robust ML data pipelines.
- AI/ML Expertise: Solid knowledge of RAG architectures, LLM fine-tuning, intent classification, and recommendation engines - applying ML algorithms and Model-Based Design (including MCP tooling and structured agentic execution paths) to multi-agent orchestration, contextual reasoning, and workflow optimization.
- Data Analysis & Feature Engineering: Experience leading EDA on multi-tenant execution logs to validate prompt/response accuracy and optimize tool usage, paired with strong feature engineering skills for vector embeddings, feature stores, and multi-tenant data isolation.
- Model Lifecycle: Experience leading model building (foundation model adaptation, prompt engineering, fine-tuning, low-latency inference) and deep understanding of the model development lifecycle, including MLOps platforms (Kubeflow, MLflow, Airflow, SageMaker) for monitoring, latency tracking, and retraining at scale.
- Technical Stack: Strong proficiency in Python and ML libraries (PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn, LangChain, LangSmith, DSPy), with experience deploying scalable ML systems in cloud-native production using Docker, Kubernetes, gRPC/REST APIs, and CI/CD under strict high-availability (99.9% uptime) and low-latency SLAs.
- Team Collaboration: Strong ability to mentor team members, drive cross-functional technical alignment, draft system design specs/model cards, and communicate technical and governance decisions.
- Education: Bachelor's in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering; Master's or PhD strongly preferred.
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: $192,000 USD - $288,000 USD
Additional US Location(s) Base Pay Range: $163,000 USD - $288,000 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.
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If you require a reasonable accommodation, you may email accommodations@workday.com, as far in advance as possible.
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