This part felt fine until they started poking at specific choices I'd made, like why I picked one architecture over another.
Select 2-3 projects that best demonstrate your ML engineering depth, and for each, follow a structured narrative: problem, approach, key decisions with trade-offs, and measurable impact. Emphasize the 'why' behind decisions—especially trade-offs between model complexity, latency, scalability, and business constraints—to show engineering maturity.
Pro tip: Quantify the impact of your decisions (e.g., 'reduced latency by 40% while maintaining 95% of accuracy') and explicitly state what you would do differently today, demonstrating growth and self-awareness.
Briefly describe the project's goal, your role, and the business or technical constraints (e.g., data size, latency requirements, team size).
Summarize the ML/NLP techniques used (e.g., BERT fine-tuning, feature engineering) and why they were chosen over alternatives.
Detail 2-3 critical decisions (e.g., model architecture, data augmentation, deployment strategy) and the trade-offs considered (accuracy vs. speed, cost vs. scalability).
Quantify outcomes (e.g., accuracy improvement, latency reduction, cost savings) and tie them back to business metrics.
Discuss what you learned, what you would do differently, and how it shaped your subsequent work.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining each tokenization method and its core algorithm, then compare them along key dimensions like vocabulary size, OOV handling, and computational efficiency. Finally, discuss practical trade-offs and give concrete examples of when each is preferred, tying back to real-world models and tasks.
Pro tip: Mention that subword tokenization methods like BPE and WordPiece are not just about handling OOV but also about balancing sequence length and vocabulary size, which directly impacts model latency and memory—critical for production systems at scale.
Briefly explain word-level, character-level, BPE, WordPiece, and SentencePiece, highlighting their core mechanism (e.g., frequency-based merging vs. likelihood-based).
Contrast them on vocabulary size, OOV handling, sequence length, computational cost, and language independence.
Explain the pros and cons of each, such as word-level's simplicity vs. OOV issues, and subword methods' balance between granularity and efficiency.
Give specific scenarios where each is appropriate, e.g., character-level for noisy text or small vocab, BPE for neural machine translation, WordPiece for BERT, SentencePiece for multilingual models.
Summarize how to choose based on task, language, and resource constraints, and mention that modern NLP often defaults to subword methods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as a two-stage retrieval-then-ranking pipeline, then explain how an LLM can enhance each stage (e.g., semantic retrieval, prompt-based ranking, or generative recommendations). Emphasize evaluation with offline metrics and online A/B tests, and address cold-start by leveraging LLM's world knowledge and few-shot prompting.
Pro tip: Show awareness of latency and cost trade-offs: propose using a smaller distilled model for retrieval and a larger LLM only for final ranking or generation, and mention caching or precomputation for frequent queries.
Ask about scale, latency, cost, and whether the system is for search, feed, or ads. Define success metrics (e.g., CTR, engagement, diversity).
Use an LLM to encode items and queries into dense vectors, then perform approximate nearest neighbor search. Optionally use LLM-generated keywords or summaries for hybrid retrieval.
Craft prompts that include user history, item metadata, and task instructions. Use the LLM to score or rank candidates, or generate recommendations directly. Consider fine-tuning or few-shot examples.
Offline: use recall@k, NDCG, and LLM-as-judge. Online: A/B test with guardrail metrics. Iterate on prompts, retrieval, and ranking based on results.
Leverage LLM's world knowledge to infer preferences from minimal context (e.g., demographics, session context). Use few-shot prompting with similar users or ask clarifying questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.