1 day ago
Base Salary
$163k - $288k/yr
Responsibilities
- Architect, deploy, and maintain end-to-end machine learning pipelines covering distributed ingestion, feature engineering, model training, evaluation, and production deployment.
- Design LLM-driven recommendation features and algorithms that optimize agentic workflows and tool usage.
- Optimize real-time model inference and scoring microservices for sub-second responses in high-concurrency, multi-tenant environments.
- Integrate telemetry and tracing into ML pipelines to monitor model performance, prompt and response drift, and agent execution efficiency.
- Use big data platforms and feature-store technologies for high-dimensional feature retrieval and analytics.
- Lead cross-functional technical alignment and translate business and governance requirements into scalable ML solutions.
- Mentor team members and create system design specifications, model cards, and technical and governance communications.
Requirements
- 8+ years of experience researching, developing, and implementing advanced machine learning models and techniques, including at least 4+ years building, deploying, and operating production machine learning systems at scale.
- 5+ years of experience with deep learning, natural language processing, and GenAI/LLM orchestration.
- Proficiency with PyTorch, TensorFlow, Scikit-learn, LangChain, LlamaIndex, and at least one of Java, Go, Scala, or C++.
- Experience with distributed processing using Spark, Flink, and Kafka, plus columnar or vector storage such as ClickHouse, Pinecone, or Milvus.
- Knowledge of RAG architectures, LLM fine-tuning, intent classification, recommendation engines, MCP tooling, and multi-agent orchestration.
- Experience with exploratory data analysis, feature engineering, vector embeddings, feature stores, and multi-tenant data isolation.
- Experience with model building, foundation-model adaptation, prompt engineering, fine-tuning, low-latency inference, and the ML development lifecycle.
- Experience with Kubeflow, MLflow, Airflow, or SageMaker for monitoring, latency tracking, and retraining at scale.
- Strong proficiency in Python, NumPy, Pandas, Scikit-learn, LangChain, LangSmith, and DSPy.
- Experience deploying scalable ML systems using Docker, Kubernetes, gRPC/REST APIs, and CI/CD under high-availability and low-latency requirements.
- Bachelor’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering required; Master’s degree or PhD strongly preferred.
- Ability to mentor colleagues, drive cross-functional technical alignment, and communicate technical and governance decisions.
Benefits
- Flexible work model requiring at least 50% of each quarter in the office or in the field, with flexibility for remote home-office roles.
- Role is based primarily in Pleasanton, California, with additional US locations available.
- May be eligible for the Workday Bonus Plan or role-specific commission/bonus and annual refresh stock grants.
- Workday offers comprehensive benefits and reasonable accommodations for qualified applicants.
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.