Sr ML Software Development Engineer, Admin Central

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.

As a Sr Software Development Engineer (ML/AI) on the Admin Central team, you will lead the technical design, development, and scaling of advanced ML pipelines, predictive analytics engines, and AI-driven recommendation features.  Admin Central sits at the core of Workday's enterprise AI and automation strategy, serving over 700 customer accounts to handle ad hoc and ambient agentic requests through high-throughput analytics and agentic workflows.

In this role, you will bridge the gap between production-grade distributed software engineering and modern ML/LLM architectures. You will architect low-latency inference services, build automated feature stores and retraining pipelines over big data infrastructure (Kafka, Spark/Flink, ClickHouse, S3), and extend AI observability across agent execution loops using frameworks like LangSmith and LangChain.

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.
  

Basic Qualifications

• 8+ years in ML research, development, and implementation of advanced models/techniques, including 4+ years building, deploying, and operating production ML systems at scale.
• 5+ years in deep learning, NLP, and GenAI/LLM orchestration using frameworks like PyTorch, TensorFlow, Scikit-learn, LangChain, and LlamaIndex, plus 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.
  

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.

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