Sr ML Engineer, Admin Central

Workday, Inc.; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

Workday, Inc. in the United States is seeking a Senior ML Engineer to design and operate end-to-end ML pipelines and agentic AI features that power our enterprise AI platform.

You will lead development across data ingestion, feature stores, model training, and production deployment, while collaborating with product teams and compliance to ensure scalable, reliable, and ethical AI solutions.

Qualifications

• 8+ years of experience in machine learning development and production systems.
• 5+ years in deep learning, NLP, and GenAI/LLM orchestration using modern frameworks.
• Experience with distributed data pipelines and scalable ML infrastructure.

Responsibilities

• Architect, deploy, and maintain end-to-end ML pipelines across data ingestion, feature engineering, training, evaluation, and deployment.
• Design algorithms and LLM-driven features to optimize agentic workflows.
• Optimize real-time inference and scaling for high-concurrency, multi-tenant environments.
• Integrate telemetry for model performance, drift, and efficiency using OpenTelemetry/LangChain/LangSmith.
• Collaborate with product, data engineering, SRE, and compliance to translate requirements into scalable ML solutions.

Skills

ML engineering

Deep learning

NLP

GenAI/LLM orchestration

Backend programming

Education

Bachelor's degree in CS/Math

Tools

PyTorch

TensorFlow

Scikit-learn

LangChain

LlamaIndex

Java

Job description

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 l