Machine Learning Engineer Interview Questions

Showing 181–202 of 202 results

• UberEasy·Machine Learning Engineer
  Explain and test completion-rate gaps

  This question evaluates understanding of causal inference, product-metrics analysis, experimental design, and statistical hypothesis testing (including two-sample t-tests) as applied to operational problems in a marketplace.

  Machine LearningTechnical Screen
• UberMedium·Machine Learning Engineer
  Implement 1D convex minimization in Python

  An Uber Machine Learning Engineer technical-screen question on derivative-free 1D convex optimization: minimize a black-box convex function on a closed interval using only function evaluations. It tests method selection (golden-section vs ternary search), stopping criteria and tolerances, function-evaluation and time/space complexity analysis, and robust handling of flat regions, boundary optima, noisy evaluations, and numerical precision.

  Machine LearningTechnical Screen
• UberMedium·Machine Learning Engineer
  Implement CLIP Contrastive Loss

  This question evaluates understanding and implementation of contrastive representation learning concepts—specifically similarity matrices, symmetric image-text contrastive loss, normalization, temperature scaling, and label construction—testing competency in building losses for embedding alignment in the Machine Learning domain.

  Machine LearningTechnical Screen
• UberMedium·Machine Learning Engineer
  Implement Multi-Head Self-Attention

  This question tests the ability to implement the multi-head self-attention mechanism from scratch using PyTorch, focusing on tensor manipulation, linear projections, and the scaled dot-product attention formula.

  Machine LearningTechnical ScreenPyTorchDeep LearningTransformers
• UberMedium·Machine Learning Engineer
  Implement linear and logistic regression

  This question evaluates understanding and hands-on implementation of linear and logistic regression, testing competencies in prediction functions, loss formulation, gradient computation, gradient descent training, regularization, and model selection within the Machine Learning domain (supervised learning and statistical modeling).

  Machine LearningTechnical Screen
• UiPathEasy·Machine Learning Engineer
  Explain Core ML Fundamentals

  This question tests your conceptual understanding of how fundamental components of neural networks and predictive models affect optimization, generalization, and performance.

  Machine LearningTechnical ScreenGradient DescentRegularizationActivation Functions
• UiPathMedium·Machine Learning Engineer
  Explain Transformer Layers and FFN Rationale

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Transformer Layers and FFN Rationale states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• UiPathMedium·Machine Learning Engineer
  LLM fundamentals: Transformer internals, beam search decoding, and LoRA fine-tuning

  An LLM fundamentals question that asks you to explain Transformer internals such as scaled self-attention, multi-head attention, positional information and causal masking. It also covers how beam search decoding compares with greedy decoding and sampling, and how LoRA reduces the cost of fine-tuning.

  Machine LearningTechnical Screen
• UiPathMedium·Machine Learning Engineer
  Reduce overfitting and explain the mean squared error loss

  A machine learning phone screen on how to diagnose and reduce overfitting, and on the mean squared error loss: its definition, what minimizing it estimates, its probabilistic interpretation, and when another loss works better. Tests fundamentals, mechanisms and trade-off reasoning.

  Machine LearningHR Screen
• UiPathMedium·Machine Learning Engineer
  Reliable LLM tool calling: format control, failure recovery, and runaway loops

  An LLM agent question on tool calling: how a model invokes tools, how to keep tool calls in a valid format, how to make tool use stable and fix wrong calls, and how to handle tool-call loops that run too long. It tests practical reliability engineering for production agents.

  Machine LearningTechnical Screen
• VoleonMedium·Machine Learning Engineer
  Audit and Extend a Time-Series ML Pipeline

  Audit a time-series ML pipeline for leakage, tune with chronological validation, and add inference with saved preprocessing and timestamp-correct features.

  Machine LearningTechnical Screen
• VoleonHard·Machine Learning Engineer
  Explain ML and statistical modeling

  This problem tests your theoretical understanding of statistical modeling and machine learning, specifically focusing on the implications of loss function selection, handling heteroskedasticity, and the trade-offs between bias and variance in predictive modeling.

  Machine LearningTechnical ScreenStatisticsRegressionOptimization
• VoleonHard·Machine Learning Engineer
  Explain a Markov Decision Process

  Review the components and trajectory semantics of an MDP, including state sufficiency, policies, transitions, rewards, and discounted return.

  Machine LearningTechnical Screen
• VoleonHard·Machine Learning Engineer
  Explain the Bellman Equation

  Understand Bellman expectation and optimality equations through one-step return decomposition, fixed points, and reinforcement-learning algorithms.

  Machine LearningTechnical Screen
• VoleonHard·Machine Learning Engineer
  Fine-Tune a Language Model on Question-Answer Pairs

  Review the full workflow for supervised language-model fine-tuning on question-answer pairs, including masking, leakage control, evaluation, and rollout.

  Machine LearningTechnical Screen
• Walmart LabsEasy·Machine Learning Engineer
  Design an LLM agent with RAG and tools

  This problem tests the ability to architect an LLM-based agent capable of retrieving information and performing actions, focusing on orchestration, tool execution, and system reliability.

  Machine LearningOnsiteLLMRAGAgentic-Workflow
• XPengHard·Machine Learning Engineer
  Explain PPO and Transformer basics

  This question evaluates mastery of reinforcement learning algorithms (PPO, policy gradients, TRPO), value-function theory and actor–critic methods (Bellman equations), on- versus off-policy trade-offs and training-stability techniques (clipping, entropy, advantage estimation), plus sequence-modeling fundamentals with Transformers (self-attention, positional encodings and complexity comparisons). It is commonly asked to judge reasoning about algorithmic trade-offs, sample efficiency and stability in practical ML systems, and falls under the Machine Learning domain—specifically reinforcement learning and deep learning/sequence modeling—testing both conceptual understanding and practical application.

  Machine LearningTechnical Screen
• XXometryMedium·Machine Learning Engineer
  Explain an End-to-End ML Project

  This question evaluates leadership and end-to-end machine learning skills, including problem framing, metric definition, data handling, model selection, training and validation practices, deployment engineering, and measurement of business impact.

  Machine LearningTechnical Screen
• YahooEasy·Machine Learning Engineer
  ML Concepts: Overfitting, Bias-Variance Trade-off, Class Imbalance, Missing Data

  A machine learning fundamentals round covering overfitting and underfitting, the bias-variance trade-off, class imbalance, and missing data. It tests whether a candidate can define each concept precisely, diagnose it from training and validation evidence, and choose remedies while weighing their side effects.

  Machine LearningTechnical Screen
• ZZillowMedium·Machine Learning Engineer
  Explain why LLMs produce hallucinations

  This question evaluates a candidate's understanding of the causes and mitigation of large language model hallucinations, covering competencies in probabilistic training objectives, data characteristics, model and optimization limitations, inference-time behavior, and awareness of mitigation strategies.

  Machine LearningTechnical Screen
• xAIHard·Machine Learning Engineer
  Design a house-price prediction workflow

  Design a house-price prediction workflow evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• xAIMedium·Machine Learning Engineer
  Explain Data Collection Choices and Their Impact on Downstream Model Performance

  A machine learning screening question: explain how the data for a project on your resume was collected and processed, then discuss how data choices affect performance on downstream tasks. It tests first-hand knowledge of data pipelines, causal reasoning about data quality, and how to measure data effects.

  Machine LearningOnsite