Staff Backline Engineer

DATABRICKS ASIAPAC UNIFIED ANALYTICS PTE. LTD.; Singapore; Híbrido; SGD 180.000 - 240.000; Jornada completa

Descripción de la vacante

Databricks AsiaPac Unified Analytics Pte. Ltd. is seeking a Staff Backline Engineer to serve as a technical escalation point for the most complex ML/AI issues.

You will collaborate with Support, Engineering, Product, and customers to diagnose, reproduce, and resolve problems across training, inference, and deployment workflows. Key focus areas include Spark, MLflow, feature engineering, and model serving, with strong Python development and a deep understanding of distributed workloads.

Formación

• Deep expertise in debugging distributed ML/AI workloads across code, frameworks, infra, and platform.
• Hands-on Python development for ML workloads with PyTorch, TensorFlow or Scikit-Learn.
• Strong knowledge of Databricks ML/AI tech, including MLflow, Model Serving, Feature Engineering, Spark MLlib, and lifecycle management.
• Proficient Spark knowledge: DataFrames, execution, memory management, shuffles, performance.
• Experience troubleshooting training/inference performance, CPU/GPU, memory, bottlenecks, concurrency, latency, and distribution.
• Experience with deployment/infrastructure: Kubernetes, cloud ML platforms, CI/CD, monitoring, production ML systems.
• Ability to read code/logs/traces/metrics and deduce root causes beyond docs.
• Demonstrated ability to own high-impact technical problems end-to-end.
• Strong technical communication to influence Eng, Product, Support, and customers.

Responsabilidades

• Serve as senior escalation point for complex ML/AI issues across model training, inference, serving, and feature engineering.
• Conduct deep technical investigations using logs, traces, metrics, profiling, and workloads to identify root causes.
• Reproduce issues via hands-on experiments, Python/Spark development, workload construction, configuration changes, and performance analysis.
• Troubleshoot training and inference failures, performance degradation, resource utilization, and distributed execution problems.
• Collaborate with Engineering and Product to drive issues to resolution and inform product improvements.
• Identify recurring failure patterns and develop better diagnostics, tooling, and automation.
• Mentor engineers and raise troubleshooting capabilities within Support.
• Act as technical SME for ML/AI platform areas and participate in cross-functional initiatives with global impact.

Conocimientos

Python

Distributed ML/AI

PyTorch

TensorFlow

Scikit-Learn

Spark

MLflow

Kubernetes

Technical communication

Troubleshooting

Herramientas

Databricks

Kubernetes

CI/CD

Descripción del empleo

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.