Staff Backline Engineer – ML/AI

• 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.

What We Look For

• 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.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range

$170.40—$255.60 USD