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Staff Backline Engineer – ML/AI

Databricks· Backline Engineering
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Bellevue, WA, San Francisco, CA$170.4k - $255.6kfull-timePosted 1mo ago

About the role

Staff Backline Engineer serving as technical expert and escalation point for complex ML/AI issues, collaborating with Support, Engineering, Product, and customers to troubleshoot, reproduce, identify root causes, and drive resolution.

Responsibilities

  • Serve as a senior escalation point for complex ML/AI issues involving model training, inference, Model Serving, MLflow, Feature Engineering, with knowledge of Spark, Delta Lake, and distributed workloads.
  • Perform deep technical investigations using logs, traces, metrics, profiling, configuration, source code, and customer workloads to identify root cause.
  • 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 closely with Engineering and Product teams to drive difficult issues to resolution and influence product improvements.
  • Identify recurring failure patterns and turn them into better diagnostics, documentation, tooling, automation, and Claude skill capabilities.
  • Mentor engineers and raise 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 with global impact.

Qualifications

  • 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 the ability to build, modify, and debug ML workloads using frameworks such as 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 optimisation.
  • Experience troubleshooting training and inference performance, including CPU/GPU utilisation, 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 behaviour rather than relying solely on documentation or configuration recommendations.
  • Demonstrated ability to independently own ambiguous, high-impact technical problems and drive them from symptom → investigation → root cause → resolution.
  • Strong technical communication skills and the ability to influence Engineering, Product, Support, and customers.

Skills

Required:Distributed ML/AI troubleshootingPythonML frameworksSpark / Spark MLlib
Nice to have:ML deployment & infrastructure

Benefits

  • Benefits information available via regional link (as referenced in job description)
Last updated Sep 17, 2026. Interested? Apply on the company site.

About Databricks

Leading Data and AI Platform for Enterprises

Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform.

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