Staff Backline Engineer (ML/AI)

Location: Bellevue, Washington, or San Francisco, California, United States

Salary: $170.40–$255.60 USD

Company

Databricks builds a data and AI infrastructure platform used to develop and scale data, analytics, machine learning, and AI applications.

What you will do

• Serve as a senior escalation point for complex ML/AI issues involving model training, inference, Model Serving, MLflow, Feature Engineering, Spark, and Delta Lake.
• Investigate logs, traces, metrics, profiling data, configurations, source code, and customer workloads to identify root causes.
• Reproduce customer issues through Python and Spark development, workload construction, configuration changes, and performance analysis.
• Troubleshoot training and inference failures, resource utilization, memory and CPU/GPU issues, distributed execution problems, and deployment failures.
• Partner with Engineering and Product to resolve difficult issues, improve products, and develop diagnostics, tooling, automation, and documentation.
• Mentor engineers and strengthen the technical troubleshooting capabilities of the Support organization.

Requirements

• 10+ years of relevant experience with deep expertise in at least one specialized track: Data Engineering, Product Supportability, or AI.
• Deep troubleshooting experience with distributed ML/AI systems across application code, frameworks, infrastructure, and the Databricks platform.
• Strong hands-on Python experience and experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-Learn.
• Strong knowledge of Databricks ML/AI technologies, Apache Spark, distributed computing, memory management, performance analysis, and model lifecycle management.
• Experience with ML deployment infrastructure, including Kubernetes, cloud ML platforms, CI/CD, model monitoring, and production ML systems.
• Strong technical communication skills and the ability to manage customers, technical stakeholders, and ambiguous high-impact problems.

Nice to have

• Expertise in large-scale data engineering and ETL pipelines using Spark, Delta Lake, or Hive.
• Code-level root-cause analysis and profiling using Java, Scala, or Python, including metrics and heap/thread dumps.
• Experience with large-scale machine learning, generative AI, LLM applications, agent-driven workflows, and distributed ML optimization.

Culture & Benefits

• Benefits and perks are offered according to the employee's region.
• Work focuses on customer success, technical excellence, automation, and operational improvement.
• Collaborate across Support, Engineering, Product, and customer organizations.
• Inclusive hiring practices and a commitment to diversity and equal employment opportunity.