Applied Scientist Interview Questions

Showing 31–60 of 75 results

• AmazonMedium·Applied Scientist
  Explain why CTR rises but CVR unchanged

  This question tests the ability to diagnose Simpson's Paradox or composition shifts in funnel metrics when engagement increases without a corresponding lift in final conversion.

  Analytics & ExperimentationOnsiteAnalyticsExperimentationFunnel Analysis
• AmazonMedium·Applied Scientist
  Find shortest path in a grid with obstacles

  This question evaluates understanding of pathfinding in grid-based environments, graph traversal concepts, and algorithmic complexity when navigating obstacles and boundary constraints in an unweighted 2D search space.

  Coding & AlgorithmsOnsite
• AmazonMedium·Applied Scientist
  Find shortest transformation steps in a word graph

  This question evaluates a candidate's ability to model string transformations as a graph, apply graph traversal and shortest-path reasoning, and manage efficient string-based search and data-structure choices within the Coding & Algorithms domain.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Applied Scientist
  Find two numbers that sum to target

  This question evaluates a candidate's competency in basic algorithmic problem-solving within the Coding & Algorithms domain, focusing on array manipulation, pair-sum reasoning, and analysis of time-space trade-offs.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Applied Scientist
  From Early NLP Embeddings to Modern LLM Embeddings

  Evaluate the architectural and practical evolution of NLP text embeddings, and formulate a decision framework comparing small bi-encoders versus large LLM-based embedding models based on retrieval quality, latency, and operational cost.

  Machine LearningOnsiteEmbeddingsNLPInformation Retrieval
• AmazonHard·Applied Scientist
  GRPO Deep Dive: Critic-Free RL, Parallelism, MLA, and Reward Design for a Reasoning LLM

  This question tests your deep understanding of Group Relative Policy Optimization (GRPO) for post-training reasoning models, including how critic-free optimization alters training dynamics, distributed memory scaling, KV-cache efficiency via Multi-Head Latent Attention (MLA), and reward hacking mitigations.

  Machine LearningTechnical ScreenReinforcement LearningLLM Post-TrainingGRPO
• AmazonMedium·Applied Scientist
  Highest Average-Salary Team in an Org Chart

  Tests bottom-up tree traversal to compute subtree aggregates (sum and count) and identify an optimal node using multi-level tie-breaking rules.

  Coding & AlgorithmsTechnical Screentreedfsrecursion
• AmazonMedium·Applied Scientist
  Implement K-means and solve interval/frequency tasks

  This multi-part question evaluates understanding of K-means clustering, interval-merging algorithms, and frequency-based top-k retrieval, assessing competencies in unsupervised learning concepts, algorithmic problem solving, data structures, and complexity analysis.

  Coding & AlgorithmsOnsite
• AmazonMedium·Applied Scientist
  Implement Multi-Head Attention from Scratch in NumPy

  This problem tests your understanding of Transformer components by requiring an implementation of Multi-Head Scaled Dot-Product Attention using only NumPy, emphasizing matrix manipulations and numerical stability.

  Coding & AlgorithmsOnsiteTransformersNumPyAttention
• AmazonHard·Applied Scientist
  Implement Optimal Bucket Batching

  This question evaluates algorithm design and optimization skills, focusing on combinatorial partitioning and batching strategies, efficient utilization of parallel GPUs, and time/space complexity analysis relevant to ML data pipelines.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Applied Scientist
  Implement PyTorch training loop

  This question evaluates practical implementation skills in PyTorch, focusing on model and device management, batch-wise training mechanics, gradient handling, and optimizer interaction.

  Coding & AlgorithmsOnsite
• AmazonMedium·Applied Scientist
  Implement SGD for linear regression and derive gradients

  This question evaluates understanding of gradient-based optimization and linear model training, including competency in deriving gradients for mean squared error and implementing stochastic (or mini-batch) gradient descent for parameter estimation.

  Machine LearningTechnical Screen
• AmazonMedium·Applied Scientist
  Implement Top-p (Nucleus) Sampling in NumPy

  This problem tests your ability to implement the nucleus sampling algorithm from scratch using NumPy, focusing on probability distribution processing and efficient array masking.

  Coding & AlgorithmsOnsiteNumPyNLPProbability
• AmazonMedium·Applied Scientist
  Implement binary search lower/upper bounds

  Implement lower_bound and upper_bound on a non-decreasing sorted array using iterative binary search in O(log n) time and O(1) space. This Amazon ML engineer screen tests precise boundary handling, loop-invariant reasoning, and edge cases like duplicates, empty arrays, and out-of-range targets.

  Coding & AlgorithmsTechnical Screen
• AmazonMedium·Applied Scientist
  Implement decoder-only GPT-style transformer

  This question evaluates implementation-level mastery of transformer architectures, including multi-head masked self-attention, position-wise feed-forward networks, residual connections, and precise tensor-shape reasoning for a decoder-only language model, within the Coding & Algorithms domain of deep learning and neural network engineering.

  Coding & AlgorithmsOnsite
• AmazonMedium·Applied Scientist
  Implement integer division without using division

  This question evaluates proficiency in implementing low-level integer arithmetic and algorithmic optimization, including correct handling of overflow and edge cases within 32-bit signed integer constraints.

  Coding & AlgorithmsOnsite
• AmazonMedium·Applied Scientist
  Implement inventory allocation with backorders

  Implement inventory allocation with backorders evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsOnsite
• AmazonMedium·Applied Scientist
  LLM Fundamentals: Tokenization Design and KL-Regularized SFT

  This problem tests your understanding of subword tokenization strategies and the theoretical motivation behind KL-divergence constraints during supervised fine-tuning.

  Machine LearningOnsiteLLMTokenizationSFT
• AmazonEasy·Applied Scientist
  List hyperparameter tuning methods

  This question evaluates understanding of hyperparameter tuning methods, including the competencies to compare different search strategies, analyze trade-offs in efficiency and parallelization, and design experiments to optimize model performance.

  Machine LearningTechnical Screen
• AmazonMedium·Applied Scientist
  Mean and Variance Through Maximum Likelihood

  Derive the maximum likelihood estimators (MLE) for the mean and variance of a univariate Gaussian distribution, and analyze the bias of the resulting variance estimator.

  Statistics & MathOnsitemaximum-likelihoodgaussian-distributionbias-variance
• AmazonMedium·Applied Scientist
  Merge Overlapping Time Ranges

  This problem tests your ability to manipulate arrays of intervals by sorting and merging, evaluating your proficiency with pointer manipulation and interval arithmetic.

  Coding & AlgorithmsTechnical ScreenArraysSortingIntervals
• AmazonHard·Applied Scientist
  Prepare For An Applied Science Manager Project And Leadership Screen

  Prepare for an applied science manager phone screen with a search-ranking project deep dive and leadership behavioral questions. The prompt helps candidates connect model decisions, A/B tests, business impact, team coaching, conflict resolution, and measurable outcomes.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Applied Scientist
  Respond to an Aggressive Stakeholder Proposal

  Tests your ability to manage high-stakes disagreements with demanding stakeholders by aligning on core goals, evaluating trade-offs with data, and delivering a scientifically sound path forward.

  Behavioral & LeadershipTechnical ScreenBehavioralStakeholder ManagementAmazon LPs
• AmazonMedium·Applied Scientist
  Test whether two user populations differ

  This question evaluates competency in statistical inference and multivariate analysis, covering hypothesis formulation about equal distributions, preprocessing and normalization considerations, choice of statistical tests or modeling approaches, and interpretation of significance and effect size.

  Machine LearningOnsite
• BByteDanceMedium·Applied Scientist
  Explain GRPO for LLM Reinforcement Learning and How It Differs from PPO

  An ML question asking you to explain Group Relative Policy Optimization (GRPO) for fine-tuning large language models with reinforcement learning. It tests your grasp of group-normalized advantages, the clipped objective with a KL penalty, the contrast with PPO and its value model, and common failure modes.

  Machine LearningTechnical Screen
• BByteDanceMedium·Applied Scientist
  Implement Multi-Head Attention from Scratch

  An ML coding question asking you to implement multi-head attention from scratch in NumPy or PyTorch, from the query, key and value projections to the output projection. It tests tensor shape handling, scaled dot-product attention, masking, numerical stability and complexity analysis.

  Machine LearningTechnical Screen
• BByteDanceMedium·Applied Scientist
  Present a Past Project and Defend Its Motivation Under Challenge

  A behavioral question for a new-grad research role: present one of your own projects in about 15 minutes and defend its motivation when the interviewer challenges it early. It tests how you frame a problem, back it with evidence and baselines, and respond to pushback calmly.

  Behavioral & LeadershipTechnical Screen
• BByteDanceMedium·Applied Scientist
  Write the Cross-Entropy Loss and Compute Precision, Recall and F1

  An ML fundamentals question in two parts: write the cross-entropy loss for binary and multi-class classification and explain why it is used, then compute precision, recall, F1 and accuracy for a described imbalanced classifier. It tests exact formulas, careful arithmetic and metric choice.

  Machine LearningTechnical Screen
• MicrosoftMedium·Applied Scientist
  Fewest Distinct Values to Delete So at Least Half the Array Is Removed

  A coding question that asks for the fewest distinct values whose complete deletion removes at least half of an integer array. It tests reasoning about how often each value occurs, proving that a choice is minimal, and the exact meaning of half when the array length is odd.

  Coding & AlgorithmsOnsite
• MicrosoftMedium·Applied Scientist
  Insert Spaces to Rebuild a Sentence from Dictionary Words

  A coding question that asks you to insert spaces into a string that lost them so that every piece is a dictionary word, returning the smallest valid sentence or None when no split exists. It tests string segmentation with dictionary lookups, handling of impossible inputs, and a canonical tie-breaking rule.

  Coding & AlgorithmsOnsite