Это настоящие вакансии?
Страницы построены на каталоге реальных инженерных ролей и описаний, чтобы интервью было ближе к настоящему раунду.
Company practice
Databricks mock interviews
Выберите роль в Databricks, ответьте вслух на вопросы AI-интервьюера и получите вердикт по компетенциям после интервью.
4 ролиScalaPythonApache SparkAWS
Создать свое интервьюРоли для тренировки
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 не аффилирован с Databricks. Это независимая тренировочная страница.
FAQ
Можно ли пройти интервью голосом?
Да. ExoForm проводит живое голосовое интервью, задает уточнения и после завершения показывает оценку и разбор ответов.
Нужно ли платить сразу?
Нет. Можно начать бесплатно, а позже перейти на платный план, если нужно больше тренировок.