Staff Backline Engineer – ML/AI
$0k - $0k • Remote • Bellevue, Washington; San Francisco, California
Posted 26d ago
Job Location
Bellevue, Washington; San Francisco, California
Tech Stack
Remote Work Policy
Fully remote
Categories
Applied AI Engineer
About the job
Databricks is seeking a Staff Backline Engineer to join their critical bridge team between Frontline Support and Engineering. This role focuses on handling complex technical issues and escalations within the Data and AI ecosystem, with a strong emphasis on customer success and proactive issue resolution. The engineer will be a technical expert and escalation point for challenging ML/AI issues, working collaboratively with Support, Engineering, Product, and customers to troubleshoot, identify root causes, and drive resolutions. The team prioritizes automation and tooling to enhance troubleshooting efficiency and improve platform supportability.
Responsibilities
- Serve as a senior escalation point for complex ML/AI issues including model training, inference, Model Serving, MLflow, and Feature Engineering.
- Perform deep technical investigations using logs, traces, metrics, profiling, configuration, source code, and customer workloads to identify root causes.
- Reproduce customer issues through hands-on experimentation, Python/Spark development, workload construction, configuration changes, and performance analysis.
- Troubleshoot model training and inference failures, performance degradation, resource utilization, memory/CPU/GPU issues, distributed execution problems, and deployment/runtime failures.
- Partner with Engineering and Product teams to drive issue resolution and influence product improvements.
- Identify recurring failure patterns and develop better diagnostics, documentation, tooling, automation, and skill capabilities.
- Mentor engineers and enhance the technical troubleshooting capabilities of the broader Support organization.
- Act as a technical SME for ML/AI platform areas and contribute to cross-functional initiatives.
Requirements
- Deep troubleshooting experience with distributed ML/AI systems and the ability to debug problems across application code, frameworks, infrastructure, and the Databricks platform.
- Strong hands-on Python experience and ability to build, modify, and debug ML workloads using frameworks like PyTorch, TensorFlow, or Scikit-Learn.
- Strong understanding of Databricks ML/AI technologies, including MLflow, Model Serving, Feature Engineering, Spark MLlib, and model lifecycle management.
- Strong Apache Spark knowledge, including DataFrames, query execution, distributed computing, memory management, shuffles, and performance optimization.
- Experience troubleshooting training and inference performance, including CPU/GPU utilization, memory issues, data bottlenecks, concurrency, latency, and distributed execution.
- Experience with ML deployment and infrastructure such as Kubernetes, cloud ML platforms, CI/CD, model monitoring, and production ML systems.
- Ability to read and reason about code, logs, stack traces, metrics, traces, execution plans, and system behavior.
- Demonstrated ability to independently own ambiguous, high-impact technical problems and drive them from symptom to resolution.
- Strong technical communication skills and ability to influence Engineering, Product, Support, and customers.
Benefits
- Eligibility for annual performance bonus
- Equity
- Health insurance
- Paid time off