Sr ML Software Development Engineer, Admin Central

Workday•Pleasanton, CA

1d•$163,000 - $288,000•Hybrid

About The Position

As a Sr Software Development Engineer (ML/AI) on Workday’s Admin Central team, you will lead the technical design, development, and scaling of advanced machine-learning pipelines, predictive analytics engines, and AI-driven recommendation features. Admin Central serves more than 700 customer accounts and handles ad hoc and ambient agentic requests through high-throughput analytics and agentic workflows. The role bridges production-grade distributed software engineering with modern ML and LLM architectures. You will architect low-latency inference services, build automated feature stores and retraining pipelines over big-data infrastructure, and extend AI observability across agent execution loops. The team is building a unified administrator experience and mission-critical distributed systems, high-availability usage pipelines, agentic functionality, and event-driven data flows across global product regions to support enterprise billing integrity at scale.

Requirements

At least 8 years of experience in ML research, development, and implementation of advanced models or techniques, including at least 4 years building, deploying, and operating production ML systems at scale.
At least 5 years of experience in deep learning, NLP, and GenAI/LLM orchestration using frameworks such as PyTorch, TensorFlow, Scikit-learn, LangChain, and LlamaIndex.
Proficiency in Java, Go, Scala, or C++ for high-performance backend systems.
Strong ability with distributed processing technologies such as Spark, Flink, and Kafka, and high-throughput columnar or vector storage such as ClickHouse, Pinecone, and Milvus.
Knowledge of RAG architectures, LLM fine-tuning, intent classification, recommendation engines, ML algorithms, Model-Based Design, MCP tooling, and structured agentic execution paths.
Experience with multi-agent orchestration, contextual reasoning, and workflow optimization.
Experience leading exploratory data analysis on multi-tenant execution logs to validate prompt and response accuracy and optimize tool usage.
Strong feature-engineering skills for vector embeddings, feature stores, and multi-tenant data isolation.
Experience leading model building, including foundation-model adaptation, prompt engineering, fine-tuning, and low-latency inference.
Understanding of the model-development lifecycle and MLOps platforms such as Kubeflow, MLflow, Airflow, and SageMaker for monitoring, latency tracking, and large-scale retraining.
Strong proficiency in Python and ML libraries including PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn, LangChain, LangSmith, and DSPy.
Experience deploying scalable ML systems in cloud-native production using Docker, Kubernetes, gRPC/REST APIs, and CI/CD under high-availability (99.9% uptime) and low-latency SLAs.
Ability to mentor team members, drive cross-functional technical alignment, draft system design specifications and model cards, and communicate technical and governance decisions.
Bachelor's degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering.

Nice To Haves

Master's degree or PhD in a relevant field.

Responsibilities

Architect, deploy, and maintain end-to-end machine-learning pipelines, from distributed data ingestion and feature engineering through model training, evaluation, and production deployment.
Design algorithms and LLM-driven recommendation features, including IDD insights and next-best-action models, to optimize agentic workflows and tool usage.
Optimize real-time model inference and scoring microservices for sub-second response times in high-concurrency, multi-tenant environments.
Integrate telemetry and tracing, including OpenTelemetry, LangChain, and LangSmith, into ML pipelines to track model performance, prompt and response drift, and execution efficiency.
Collaborate with core backend engineers to use big-data platforms such as ClickHouse, Apache Iceberg, and Spark for high-dimensional feature retrieval and analytics.
Partner with Product Managers, Data Engineering, SRE, and Legal/Compliance to translate business and governance requirements into scalable ML solutions.
Mentor team members, drive cross-functional technical alignment, draft system design specifications and model cards, and communicate technical and governance decisions.