Applied Scientist Interview Questions

Showing 1–30 of 34 results

• AmazonMedium·Applied Scientist
  Check if adding edge creates cycle in digraph

  This question evaluates understanding of graph algorithms, reachability, and algorithmic complexity by determining whether adding a directed edge would introduce a cycle in an otherwise acyclic directed graph.

  Coding & AlgorithmsTechnical Screen
• AmazonEasy·Applied Scientist
  Describe overfitting and L1/L2 regularization

  This question evaluates understanding of model generalization and regularization techniques by probing knowledge of overfitting and L1/L2 regularization and their effects on model parameters.

  Machine LearningTechnical Screen
• AmazonHard·Applied Scientist
  Design a RAG system end to end

  This question evaluates a candidate's ability to design a production-grade Retrieval‑Augmented Generation (RAG) system, testing competencies in scalable ML system architecture, embedding and vector retrieval strategies, prompt orchestration, freshness and latency engineering, security/access controls, and evaluation metrics.

  ML System DesignTechnical Screen
• AmazonMedium·Applied Scientist
  Design an advertiser metrics tracking platform

  This question tests your ability to design a flexible, object-oriented system for ingesting, processing, and querying real-time advertising performance metrics.

  Software Engineering FundamentalsTechnical ScreenSystem DesignOOPDesign Patterns
• AmazonMedium·Applied Scientist
  Determine if all courses can be completed

  This problem tests your ability to model prerequisites as a directed graph and detect cycles to determine if a valid topological ordering exists.

  Coding & AlgorithmsTechnical ScreenGraphsTopological SortDFS
• AmazonHard·Applied Scientist
  Explain Core ML Interview Concepts

  This question evaluates core machine learning fundamentals including statistical modeling assumptions and loss functions (linear and logistic regression), ensemble methods and feature sampling in random forests, optimization algorithms (Adam versus stochastic gradient descent), and neural network capacity and training dynamics.

  Machine LearningTechnical Screen
• AmazonMedium·Applied Scientist
  Explain LLM architecture, tuning, evaluation

  This question evaluates understanding of transformer-based LLM architectures, positional embedding variants, parameter-efficient fine-tuning (PEFT) approaches, regularization strategies, and evaluation methodologies within the Machine Learning domain focused on large language models and NLP, emphasizing both conceptual understanding and practical application. It is commonly asked to assess the interviewee’s ability to reason about architecture and tuning trade-offs, model generalization and stability, and the design of reliable offline and online evaluation pipelines for production-minded language model systems.

  Machine LearningTechnical Screen
• AmazonHard·Applied Scientist
  Explain ML statistics and model design concepts

  Explain ML statistics and model design concepts evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  ML System DesignTechnical Screen
• AmazonMedium·Applied Scientist
  Explain core ML concepts and diagnostics

  This question evaluates mastery of core Machine Learning concepts and diagnostics, covering statistical inference (p-values), overfitting/underfitting and bias-variance, causal inference approaches, encoding/decoding, optimization and backpropagation, gradient stability, handling imbalanced data, evaluation metrics versus accuracy, and experimental A/B testing. It is commonly asked in ML interviews to assess breadth and depth in the Machine Learning domain and probes both conceptual understanding and practical application by testing theoretical knowledge, recognition of common failure modes, and diagnostic reasoning used to validate models and experiments.

  Machine LearningTechnical Screen
• AmazonHard·Applied Scientist
  Explain imbalance, metrics, bias-variance, Transformers vs. CNNs

  An Amazon machine-learning-engineer technical screen covering class-imbalance handling at the data, loss, and decision levels; leakage-aware validation; the bias–variance tradeoff; metric selection for rare-positive fraud data (PR-AUC, precision@k, calibration); and Transformer vs. CNN inductive biases. Includes worked numeric examples for cost-based thresholds and metric interpretation.

  Machine LearningTechnical Screen
• AmazonMedium·Applied Scientist
  Explain surprisal and its units

  This question evaluates understanding of probabilistic information measures in language modeling, specifically the concept of surprisal, its units, and its relationship to evaluation metrics such as cross-entropy and perplexity within the Machine Learning/NLP domain.

  Machine LearningTechnical Screen
• 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
• 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
• 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 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 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
• 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
  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
• 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
• NVIDIAMedium·Applied Scientist
  How AI Agents Fit Into Your Research Workflow: Ideas, Experiments, and Paper Writing

  A research internship interview question about how AI agents change a researcher's daily workflow across generating ideas, running experiments, and writing papers. It tests concrete delegation choices, how agent output is verified, awareness of failure modes such as fabricated references, and judgment about confidentiality and reproducibility.

  Behavioral & LeadershipTechnical Screen
• WayfairMedium·Applied Scientist
  Set Bids for Search Advertising from Business Value

  Design search-ad bidding from expected business value, with budget pacing, delayed conversion feedback, auction assumptions, and incremental-profit evaluation.

  ML System DesignTechnical Screen
• EeBayEasy·Applied Scientist
  Behavioral: beyond-scope ownership, manager conflict, and a skip-level deadline at risk

  A behavioral question set on taking on work beyond your scope, handling a disagreement with your manager when discussion fails, and responding when a task from your skip-level manager will miss its deadline. It tests ownership, managing up, escalation judgment and communicating risk early.

  Behavioral & LeadershipTechnical Screen
• EeBayEasy·Applied Scientist
  Behavioral: choosing a large cross-time-zone company, role fit, and on-call motivation

  A behavioral question set for a machine learning role on why you want a large company with cross-time-zone teams rather than a small one, why this position, and how you stay motivated when the role carries heavy on-call duty. It tests genuine motivation, self-awareness and ownership of production systems.

  Behavioral & LeadershipTechnical Screen