Staff Backline ML/AI Engineer — Escalations & Debugging

Fdetimes; Bellevue (WA); On-site; USD 235,000 - 352,000; Full time

Job summary

Databricks is seeking a Staff Backline Engineer to serve as a technical SME and escalation point for complex ML/AI issues. You will work across Support, Engineering, Product, and customers to diagnose, reproduce, and drive issues to resolution.

You will mentor engineers, influence product improvements, and contribute to cross-functional initiatives impacting global teams. Strong Python + Spark expertise is required and familiarity with MLflow, Model Serving, and Kubernetes is highly valued.

Qualifications

• Deep troubleshooting experience with distributed ML/AI systems across application code, frameworks, infrastructure, and Databricks platform.
• Strong hands-on Python experience and ability to build, modify, and debug ML workloads using PyTorch, TensorFlow, or Scikit-Learn.
• Strong knowledge of Databricks ML/AI technologies including MLflow, Model Serving, Feature Engineering, Spark MLlib, and model lifecycle management.
• Solid Apache Spark knowledge: DataFrames, distributed computing, memory management, shuffles, performance optimization.

Responsibilities

• Serve as a senior escalation point for complex ML/AI issues involving model training, inference, and deployment.
• Perform deep technical investigations using logs, metrics, and workloads to identify root causes.

Skills

Python

PyTorch

TensorFlow

Scikit-Learn

Spark

MLflow

Model Serving

Feature Engineering

Tools

Kubernetes

CI/CD

Job description

Databricks is seeking a Staff Backline Engineer to serve as a technical SME and escalation point for complex ML/AI issues. You will work across Support, Engineering, Product, and customers to diagnose, reproduce, and drive issues to resolution.

You will mentor engineers, influence product improvements, and contribute to cross-functional initiatives impacting global teams. Strong Python + Spark expertise is required and familiarity with MLflow, Model Serving, and Kubernetes is highly valued.