Top 9
Prep plan
Updated weekly · Last refresh Oct 17

Analog Devices Research Scientist Interview Questions

The questions to prepare for a Analog Devices Research Scientist interview. Questions from real interview reports rank first. Updated daily.

9questions
~2htotal time
Track your progressSign up free to work through all 9 questions and resume where you left off.
Start practicing free →
1
CodingStart here. 3 questions · ~37 min
Complexity-Penalized PyTorch LossHard
Practice

Compute mean squared prediction error plus an L2 complexity penalty over model parameters.

Neural NetworksbackpropagationCodingAnalog Devices
Streaming Moving AverageMedium
Practice

Compute prefix-aware moving averages efficiently with a rolling sum over sensor readings.

aggregationQueueMathAnalog Devices
Porting Python ML to C++Medium

Tests ability to anticipate performance, tooling, and numerical issues when deploying ML to embedded C++.

Neural NetworksMathArraysAnalog Devices
2
Machine Learning5 questions · ~61 min
Knowledge Distillation for TransformersHard

Design and implement knowledge distillation that transfers a large transformer's behavior to a smaller, lower-latency student model.

Hyperparameter TuningNeural Networksmodel architectureAnalog Devices
Quantization Accuracy DegradationHard

Quantify INT8 quantization error, locate accuracy loss, and compare post-training quantization with quantization-aware training.

Neural NetworksRegularizationDeep LearningAnalog Devices
FFT vs 1D CNN Trade-offsHard

Benchmark FFT-based features against a 1D CNN for time-series classification under embedded compute and memory constraints.

model architectureFeature EngineeringDeep LearningAnalog Devices
Feature Extraction From Noisy RFHard

Design an RF signal preprocessing and feature extraction pipeline for robust edge classification on Analog Devices hardware.

model selectionFeature EngineeringDeep LearningAnalog Devices
More Machine Learning questions with a free account

Sign up to see every question

Create a free account to unlock this list and practice real interview questions.

Get my prep plan
3
More topics1 question · ~12 min
Kalman Filter vs RNNHard

Explain when a classical Kalman filter is preferable to a deep recurrent neural network for noisy time-series estimation.

Bayesian ReasoningCorrelationlinear algebraAnalog Devices
The finish line: interview-readyComplete all 9 questions to finish this plan.