Are these based on real engineering roles?
Yes. The catalog is built around real engineering role patterns so the practice round feels closer to a live interview.
Company practice
Databricks mock interviews
Pick a role, answer follow-up questions out loud, and get a scored verdict after the interview.
4 rolesScalaPythonApache SparkAWS
Create custom interviewRoles to practice
Databricks
ML / AI
Senior Machine Learning Engineer
Own end-to-end model development, from research and prototyping through deployment and monitoring, building scalable ML systems and pipelines that ship into Databricks products. The role requires fluency with modern deep-learning frameworks and the data and serving infrastructure to operationalize models in production. A technical interview would probe ML system design, feature pipelines and training/serving consistency, and practical tradeoffs in model evaluation, deployment, and monitoring at scale.
PythonPyTorchMLflowDatabricks
Data
Senior Software Engineer, Distributed Data Systems
Build the next-generation distributed storage and processing engine on the Runtime team, contributing to Apache Spark, Delta Lake, and the data-plane services and query optimizer behind the Databricks Lakehouse. The work is deep systems engineering on petabyte-scale data over cloud object stores like S3 and Azure Blob. A technical interview would probe distributed-systems and database internals, query execution and optimization, and concurrency and performance engineering in Scala/Java on large datasets.
ScalaApache SparkDelta LakeDatabricks
ML / AI
Staff Backend Software Engineer, LLM Infrastructure
Build the LLM serving infrastructure that processes trillions of tokens per week across partner models (OpenAI, Anthropic, Gemini) and self-hosted open models, improving reliability, latency, and efficiency of distributed AI workloads. The role spans scalable APIs, GPU orchestration, and real-time serving systems built with tools like vLLM, Ray, and MLflow. An interview would explore designing high-throughput low-latency serving systems, GPU resource scheduling and batching strategies, and service-oriented backend architecture for inference at massive scale.
ScalaGoPythonDatabricks
Backend
Staff Software Engineer, Backend
Design and build the large-scale backend services and control plane that power the multi-cloud Databricks platform, owning critical microservices spanning provisioning, orchestration, and platform APIs. The work emphasizes reliability, scalability, and clean service architecture across AWS, Azure, and GCP. An interview would probe large-scale distributed system design, service decomposition and API design, and operating highly available, fault-tolerant backend systems in a multi-tenant cloud environment.
ScalaKubernetesAWS
ExoForm is not affiliated with Databricks. This is an independent practice page.
FAQ
Is the interview voice-based?
Yes. ExoForm runs a live voice interview, asks follow-ups, and produces structured feedback after the session.
Can I try it for free?
Yes. You can start with the free interview allowance before upgrading for more practice.