Job description

P - 1381 At Databricks, we are passionate about enabling Data & AI teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for data interaction to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. About the Team The Backline Engineering Team serves as the critical bridge between Frontline Support and Engineering. We handle complex technical issues and escalations across the Data and AI ecosystem. With a strong focus on customer success, we are committed to delivering exceptional customer satisfaction by providing deep technical expertise, proactive issue resolution, and continuous platform improvements. We emphasise automation and tooling to enhance troubleshooting efficiency, reduce manual efforts, and improve the overall supportability of the platform and the health of our products. By developing smart solutions and streamlining workflows, we drive operational excellence and ensure a delightful experience for both customers and internal teams. As a Staff Backline Engineer, you will be a technical expert and escalation point for some of the most complex ML/AI issues. You will work across Support, Engineering, Product, and customers to troubleshoot difficult problems, reproduce issues, identify root causes, and drive them to resolution. What You'll Do 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 About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

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About the Team
The Backline Engineering Team serves as the critical bridge between Frontline Support and Engineering. We handle complex technical issues and escalations across the Data and AI ecosystem.
By developing smart solutions and streamlining workflows, we drive operational excellence and ensure a delightful experience for both customers and internal teams.
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.
Our Commitment to Diversity and Inclusion

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