Lyft·Machine Learning Engineer·Technical Phone Screen·Senior

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May 2026Remote

Summary

Lyft ML Engineer interview that was basically a deep dive into one coding problem. They wanted a full one-hot encoder from scratch, no libraries, and then kept pulling the thread on edge cases and scalability until I ran out of things to say.

Questions Asked (4)

Build a one-hot encoder class from scratch (no sklearn or pandas) with fit, transform, and fit_transform methods.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with fit and just stored sorted unique categories in a list.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: should the encoder handle unseen categories, return dense or sparse output, and what data types are expected? Then implement a class that stores a mapping from category to index during fit, and uses that mapping to transform new data, handling edge cases like unseen categories and unknown tokens.

Pro tip: Mention that in production, you'd typically use scikit-learn's OneHotEncoder, but implementing from scratch demonstrates understanding of the underlying mechanics and allows customization for specific needs like handling rare categories or memory efficiency.

1. Clarify requirements and edge cases

Ask about input format (list of lists, numpy array), handling of unseen categories (ignore, error, or treat as all zeros), and output format (dense vs sparse).

2. Design the class structure

Define __init__ to optionally accept parameters like handle_unknown, and initialize attributes for categories_ and possibly a mapping dictionary.

3. Implement fit method

Iterate through the training data to collect unique categories per feature, sort them for deterministic output, and store the mapping.

4. Implement transform method

For each sample, create a one-hot vector per feature using the stored mapping, handling unseen categories according to the specified strategy.

5. Implement fit_transform and test

Combine fit and transform, then walk through a small example to verify correctness and discuss potential optimizations.

Key Points to Mention

Handling unseen categories during transform (e.g., ignore, error, or all-zeros)
Memory efficiency: using sparse matrices for high-cardinality features
Deterministic ordering of categories (e.g., sorted) for reproducibility
Input validation and type handling (e.g., strings, numbers, mixed types)
Integration with scikit-learn API (fit, transform, fit_transform, get_params, set_params)
Trade-offs between one-hot encoding and other encoding methods (e.g., target encoding, embeddings)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

How should the encoder handle categories at transform time that were never seen during fit? Walk through the tradeoffs of each approach.

Technical Trade-offsSystem Design

Author's notes

Three options: raise an error, return all zeros, or add an UNK column.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem: unseen categories at transform time break encoders that assume a fixed vocabulary. Then systematically compare approaches like error, ignore, and handle_unknown='infrequent_if_exist', discussing tradeoffs in terms of model robustness, data leakage, and production constraints. Conclude with a recommendation tailored to Lyft's dynamic, real-world data.

Pro tip: Emphasize that the right choice depends on whether unseen categories are expected in production and whether the model can tolerate them; for Lyft, where new cities or ride types emerge, a robust strategy like hashing or target encoding with smoothing is often preferable to dropping or erroring.

1. Define the problem and constraints

Clarify that unseen categories are those not present in the training data but appearing during inference. Discuss constraints like real-time serving, model retraining frequency, and the cost of errors.

2. List common approaches

Enumerate options: raise an error, ignore (treat as unknown), map to a catch-all 'other' category, use frequency-based encoding, hashing trick, or target encoding with smoothing.

3. Analyze tradeoffs for each approach

For each, discuss pros and cons: error ensures data quality but breaks production; ignore is simple but loses information; catch-all preserves signal but may dilute; hashing scales but introduces collisions; target encoding can leak but handles high cardinality.

4. Consider production and business context

Relate to Lyft's use case: new cities, ride types, or user behaviors. Weigh the impact of unseen categories on model performance and user experience.

5. Recommend a strategy

Propose a solution based on tradeoffs, such as using handle_unknown='infrequent_if_exist' in scikit-learn or a hashing encoder, and justify why it fits Lyft's needs.

Key Points to Mention

Data leakage risks when using target encoding on unseen categories
The importance of monitoring unseen category rates in production
Tradeoff between model complexity and robustness
Use of scikit-learn's OneHotEncoder with handle_unknown parameter
Hashing trick for high-cardinality and streaming scenarios
Business impact: how unseen categories affect predictions and user experience

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

What's the time and space complexity of your encoder, and how would you scale it to columns with very high cardinality?

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

Complexity part was easy, O(n*k) for transform where k is number of categories.

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AI HintsAI Generated

Suggested Approach

Start by clearly stating the time and space complexity of your encoder, then discuss the challenges of high-cardinality columns and propose scalable solutions like hashing, target encoding, or embeddings. Emphasize trade-offs between model performance, computational efficiency, and memory usage, and relate to Lyft's scale.

Pro tip: Mention that you monitor cardinality growth and have a fallback strategy for unseen categories, showing production awareness. Also, quantify the impact of your approach on model metrics and system resources.

1. State encoder complexity

Clearly specify the time and space complexity of your encoder (e.g., O(n) time for one-hot, O(n*k) space for k categories) and note any assumptions.

2. Define high cardinality problem

Explain why high cardinality is problematic: increased memory, overfitting, and computational cost, especially for one-hot encoding.

3. Propose scalable techniques

Describe methods like hashing trick, target encoding with smoothing, frequency capping, or learned embeddings, and their complexity trade-offs.

4. Discuss implementation and monitoring

Outline how you would implement and monitor the chosen approach, including handling unseen categories and updating encodings over time.

5. Evaluate trade-offs and impact

Compare techniques on model performance, latency, and memory, and justify your choice for Lyft's scale and real-time needs.

Key Points to Mention

Time and space complexity of common encoders (one-hot, ordinal, target, hashing, embeddings)
Hashing trick: fixed memory, collision handling, and O(1) lookup
Target encoding with smoothing and cross-validation to prevent leakage
Learned embeddings: dimensionality reduction and end-to-end training
Frequency capping and grouping rare categories
Monitoring cardinality drift and retraining encoders periodically

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Implement an inverse_transform method that maps encoded vectors back to their original category labels.

Algorithms & Data Structures

Author's notes

Straightforward once you have the category list.

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AI HintsAI Generated

Suggested Approach

Clarify the encoding scheme (one-hot, ordinal, or custom) and then design inverse_transform to map each encoded vector back to its original label using a stored mapping. Handle edge cases like unknown encodings, invalid inputs, and multi-dimensional arrays, and ensure the method is efficient and consistent with the forward transform.

Pro tip: Emphasize that inverse_transform must be the exact inverse of transform, and mention that storing the mapping during fit (or transform) is crucial for correctness and performance. Also, discuss how to handle unseen categories gracefully, which is often overlooked.

1. Clarify encoding scheme and requirements

Ask or state the type of encoding (e.g., one-hot, label encoding, ordinal) and the expected input/output formats. Confirm whether the method should handle single vectors or batches, and what to do with unknown or invalid encodings.

2. Design the mapping storage

During fit or transform, store a mapping from encoded representation to original label (e.g., a dictionary or array). For one-hot, this could be an array of labels indexed by the position of the 1; for ordinal, a simple list.

3. Implement the inverse mapping logic

For each encoded vector, find the corresponding original label using the stored mapping. Handle cases where the encoding is not found (e.g., return None, raise an error, or use a default).

4. Handle edge cases and validation

Validate input shapes and types, handle multiple 1s in one-hot (if applicable), and decide on behavior for unknown categories. Ensure the output shape matches the original labels.

5. Test and optimize

Write unit tests for typical and edge cases, and consider vectorizing the operation for performance if dealing with large datasets.

Key Points to Mention

Inverse mapping must be consistent with the forward transform (bijective).
Store the mapping during fit/transform to avoid recomputation.
Handle unknown or invalid encodings gracefully (e.g., return None, raise error, or use a special label).
Support batch processing and maintain correct output shape.
Consider efficiency: use vectorized operations or efficient data structures.
Document assumptions and limitations (e.g., only works for encodings seen during fit).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.