ML Mentor
Назад к подготовке
Navio
Аудиозапись технического собеседованияТехническое собеседование2025-08-06

Navio: Техническое собеседование

Dropout, BCE versus accuracy, BatchNorm in DDP, TensorRT/ONNX optimization, quantization/calibration, detector resolution and perception-team context.

Пройти собеседование

Аудио и материалы

Аудио собеседования

0:00 / 28:19

Этап 1 из 3NavioDeep Learning Researcher, Perception2025-08-06 - 2025-08-15
Собеседования в Navio: Perception DL

Техническое собеседование в Navio

Весь процессЭтап 2

Выводы и как готовиться

  • The round was practical and production-leaning rather than math-heavy: how training behavior changes at inference, how metrics can disagree, and how deployment optimizers work.
  • The automotive context matters: model quality is tied to sensor setup, small objects, latency, hardware constraints and safety-critical perception behavior.
  • The interviewer also described the next loop: a medium LeetCode-style algorithm section and final conversation with the team lead.