Sr ML Engineer, Business Process Optimization

Workday • Pleasanton, CA

1d • Hybrid

About The Position

The Senior Software Engineer on Workday’s Business Process Optimize (BPO) team leads the technical design, development, and scaling of advanced machine-learning pipelines, predictive analytics engines, and AI-driven recommendation features. The team supports Workday’s enterprise AI and automation strategy, serving more than 100 Tier-0 customer accounts and handling millions of daily inference requests through high-throughput analytics, Model Context Protocol (MCP), and agentic workflows.

The role combines production-grade distributed software engineering with modern ML and LLM architectures. It involves architecting low-latency inference services, building automated feature stores and retraining pipelines over big-data infrastructure, and extending AI observability across agent execution loops. The team builds mission-critical distributed systems, high-availability usage pipelines, and event-driven data flows across global product regions.

Requirements

• At least 8 years of experience developing, researching, and implementing advanced machine-learning models and techniques, including at least 4 years building, deploying, and operating production ML systems at scale.
• At least 5 years of experience and proficiency 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 in distributed processing with Spark, Flink, and Kafka, and high-throughput columnar or vector storage such as ClickHouse, Pinecone, or Milvus.
• Knowledge of RAG architectures, LLM fine-tuning, intent classification, recommendation engines, 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 retraining at scale.
• 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.
• Strong collaboration and communication skills, including the ability to mentor team members 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 insights and next-best-action models, to optimize agentic workflows and tool usage.
• Optimize real-time model inference and scoring microservices to provide sub-second response times in high-concurrency, multi-tenant environments.
• Integrate telemetry and tracing into ML pipelines to monitor model performance, prompt and response drift, and execution efficiency.
• Collaborate with backend engineers to use big-data platforms 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.
• Lead cross-functional technical alignment, mentor team members, draft system design specifications and model cards, and communicate technical and governance decisions.

Benefits

• May be eligible for the Workday Bonus Plan or a role-specific commission or bonus.
• May be eligible for annual refresh stock grants.
• Flexible work arrangement combining in-person and remote work.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior