2 days 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 engines, next-best-action models, and agentic workflow capabilities.
- Optimize real-time model inference and scoring microservices for sub-second responses in high-concurrency, multi-tenant environments.
- Integrate telemetry and tracing to monitor model performance, prompt and response drift, and execution efficiency.
- Use big data platforms and feature stores 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 to translate business and governance needs into scalable ML solutions.
- Mentor team members, drive technical alignment, and draft system design specifications and model cards.
Requirements
- At least 8 years of machine learning development experience, including at least 4 years building, deploying, and operating production machine learning systems at scale.
- At least 5 years of experience with deep learning, natural language processing, and GenAI/LLM orchestration.
- Proficiency in Java, Go, Scala, or C++ for high-performance backend systems, plus strong Python proficiency.
- Experience with distributed processing using Spark, Flink, and Kafka, and high-throughput storage such as ClickHouse, Pinecone, or Milvus.
- Knowledge of RAG architectures, LLM fine-tuning, intent classification, recommendation engines, MCP tooling, and structured agentic execution paths.
- Experience with exploratory data analysis, feature engineering, vector embeddings, feature stores, and multi-tenant data isolation.
- Experience with MLOps platforms including Kubeflow, MLflow, Airflow, and SageMaker.
- Experience deploying scalable ML systems using Docker, Kubernetes, gRPC, and REST APIs under high-availability and low-latency SLAs.
- Bachelor’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering required; master’s or PhD strongly preferred.
- Ability to mentor team members, lead cross-functional technical alignment, and communicate technical and governance decisions.
Benefits
- Annualized base salary ranges from $192,000 to $288,000 for Pleasanton, California, and from $163,000 to $288,000 for additional US locations; the role may also be eligible for a bonus plan, role-specific bonus, and annual refresh stock grants.
- Flex Work combines remote and in-person work, with at least 50% of each quarter spent in the office or in the field with customers, prospects, or partners.
- Remote home-office employees may also gather in company offices for important moments.
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.