Machine Learning Engineer Interview Questions

Showing 1–10 of 10 results

• EExaHard·Machine Learning Engineer
  Design a Streaming Percentile Estimator

  Design a streaming percentile estimator without storing every observation, and define rank error precisely. Compare a deterministic single-stream summary with t-digest-style centroids across memory, update cost, duplicates, skew, tail accuracy, and distributed merge limitations.

  Statistics & MathTechnical Screen
• GoogleMedium·Machine Learning Engineer
  Generate values by weighted probabilities

  This question evaluates understanding of probabilistic sampling, numerical stability, input validation, and algorithmic trade-offs for streaming weighted random draws, including generator determinism via seeding and methods such as CDF/binary-search versus alias for performance.

  Statistics & MathOnsite
• LiftoffHard·Machine Learning Engineer
  Probability That the Sum of 1,000 Fair Die Rolls Exceeds a Threshold

  A fair six-sided die is rolled 1,000 times; estimate the probability that the sum of the faces exceeds a threshold, including a threshold of 600. Tests the mean and variance of one roll, how they scale for a sum of independent rolls, the normal approximation with a continuity correction, and range checks.

  Statistics & MathOnsite
• LinkedInMedium·Machine Learning Engineer
  Transform Biased and Uniform Random Bits Exactly

  Tests the ability to design and mathematically prove exact random variate generators between biased and uniform bit sources, analyzing termination and expected sample complexity.

  Statistics & MathTechnical ScreenProbabilityAlgorithmsSampling
• OpenAIMedium·Machine Learning Engineer
  Derive MLE and Bayesian posterior for Bernoulli

  This question evaluates parameter estimation and inferential reasoning for Bernoulli/binomial data, including maximum likelihood estimation and its asymptotic variance, Bayesian updating with a Beta prior and the resulting posterior and posterior predictive probability, as well as interval estimation; it is classified in the Statistics & Math domain. It is commonly asked to assess understanding of frequentist versus Bayesian inference, the behavior and reliability of asymptotic approximations and credible intervals, and the practical implications for predictive probability and uncertainty quantification, testing both conceptual understanding and practical application.

  Statistics & MathOnsite
• OpenAIMedium·Machine Learning Engineer
  Maintain Entropy for a Streaming Distribution

  Maintain empirical Shannon entropy after every categorical observation without rescanning all categories. The discussion should cover arbitrary identifiers, first-item behavior, exact counts, floating-point drift, concurrency, deletions, sliding windows, and merging summaries from separate stream partitions.

  Statistics & MathOnsite
• StackAdaptMedium·Machine Learning Engineer
  Camel and Bananas: Maximize Bananas Delivered Across the Desert

  Classic camel-and-bananas brainteaser: a camel that carries at most 1,000 bananas and eats one per kilometer must move 3,000 bananas to a market 1,000 km away. Find the maximum number that can arrive, testing piecewise cost reasoning, depot planning, and a proof of optimality.

  Statistics & MathOnsite
• StackAdaptEasy·Machine Learning Engineer
  Explain Why a Geometric Mean Can Exceed Its Median

  Tests derivation and comparison of the expected value and median of a geometric distribution, explaining how right-skewness drives the mean above the median across different parameterizations.

  Statistics & MathTechnical Screenprobabilitystatisticsgeometric-distribution
• StackAdaptMedium·Machine Learning Engineer
  Reservoir Sampling: Uniform k-of-n Sample From a Stream of Unknown Length

  Design a one-pass algorithm that keeps a uniform random sample of k items from a stream whose length is unknown in advance, using memory proportional to k. Tests the replacement rule, an inductive proof that every item is kept with equal probability, complexity, and edge cases.

  Statistics & MathOnsite
• TeslaHard·Machine Learning Engineer
  Explain and derive importance sampling estimators

  This question evaluates understanding of importance sampling, Monte Carlo estimators, weight normalization, variance behavior, optimal proposal selection, and effective sample size, assessing competency in statistical estimation and Monte Carlo methods.

  Statistics & MathTechnical Screen