1 day ago
Base Salary
$163k - $288k/yr
Responsibilities
- Architect, deploy, and maintain end-to-end machine learning pipelines covering distributed ingestion, feature engineering, training, evaluation, and production deployment.
- Design LLM-driven recommendation features, predictive analytics, and agentic workflow capabilities such as next-best-action models.
- Optimize real-time model inference and scoring microservices for sub-second response times in high-concurrency, multi-tenant environments.
- Integrate telemetry and tracing to monitor model performance, prompt and response drift, and agent execution efficiency.
- Use big data and feature-store platforms for high-dimensional feature retrieval, analytics, and multi-tenant data isolation.
- Lead model building, foundation-model adaptation, prompt engineering, fine-tuning, low-latency inference, monitoring, and retraining.
- Partner with Product Management, Data Engineering, SRE, Legal, and Compliance, mentor teammates, and drive technical alignment through system design specifications and model cards.
Requirements
- 8+ years of experience in ML research, development, and implementation of advanced models and techniques, including 4+ years building, deploying, and operating production ML systems at scale.
- 5+ years of experience in deep learning, NLP, and GenAI/LLM orchestration.
- Proficiency in Java, Go, Scala, or C++ for high-performance backend systems.
- Experience with distributed processing and high-throughput storage using Spark, Flink, Kafka, ClickHouse, Pinecone, or Milvus.
- Knowledge of RAG architectures, LLM fine-tuning, intent classification, recommendation engines, MCP tooling, structured agentic execution, and multi-agent orchestration.
- Experience with exploratory data analysis, vector embeddings, feature engineering, feature stores, and multi-tenant data isolation.
- Experience with MLOps platforms including Kubeflow, MLflow, Airflow, and SageMaker for monitoring, latency tracking, and retraining.
- Strong proficiency in Python and ML libraries including PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn, LangChain, LangSmith, and DSPy.
- Experience deploying scalable ML systems using Docker, Kubernetes, gRPC, and REST APIs under high-availability and low-latency requirements.
- Bachelor’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering; a master’s degree or PhD is strongly preferred.
- Ability to mentor team members, communicate technical and governance decisions, and lead cross-functional technical alignment.
Benefits
- Annualized base salary is listed as $192,000-$288,000 for Pleasanton, California, and $163,000-$288,000 for additional U.S. locations; the role may also be eligible for a bonus plan, commissions or bonus, and annual refresh stock grants.
- Flexible hybrid work requires spending at least 50% of each quarter in the office or in the field with customers, prospects, and partners.
- Workday provides comprehensive benefits and reasonable accommodations, and is an equal opportunity employer.
Tech Stack
Apache AirflowApache FlinkApache KafkaApache SparkC++ClickHouseDockerGogRPCJavaKubernetesMLflowNumPyPandasPythonPyTorchScalascikit-learnTensorFlow
Categories
About Workday
Workday builds cloud-based enterprise applications for human capital management and financial management, including payroll, time tracking, expenses, planning, and procurement, sold on a subscription basis with professional services. Founded in 2005 and headquartered in Pleasanton, California, it is a public company traded on NASDAQ as WDAY. Organizations worldwide use Workday to unify HR and finance data, automate processes, and apply AI to workforce and financial operations.