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The Interview Process
Take-Home Coding
A practical assignment bridging ML and engineering, such as building a scalable semantic search API using dense embeddings from scratch.
Technical Screen
Reviewing your take-home code and a live coding session focusing on Python data structures and PyTorch/Jax efficiency.
Onsite Loop
Four interviews covering Applied ML (fine-tuning, prompt engineering evaluations), Backend Engineering, Product Sense, and Behavioral questions.
Real OpenAI Interview Questions
Practice these exact questions faced by previous Machine Learning Engineer (Applied AI/Product) candidates.
1Walk me through the exact pipeline you would build to continuously fine-tune an internal code-generation model using daily GitHub commits from the engineering team. (Applied ML / Scrappiness)
2You've deployed a new whisper model to production, but user complain the transcriptions are heavily hallucinating names. How do you mitigate this without retraining the base model? (Product Empathy / ML)
3Write a Python function to efficiently batch requests to a rate-limited language model API while maximizing throughput and handling asynchronous retries. (Coding)
4Tell me about a time you tried to use a complex Deep Learning model to solve a problem that could have been solved with a simple regex or heuristic. (Ego-lessness / Scrappiness)
5How do you design a rigorous, automated evaluation framework for an LLM feature when the 'correct' output is highly subjective? (Excellence / Evaluation)
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Offer Manager
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