Artificial Intelligence
3 · ML Engineer — Ranking, RecSys, Risk
The classical role. Still large, especially in India, and the one most often rebranded rather than changed.
What the job actually is
Models that serve live traffic: what to show, what to rank, what to block. Feed ordering, search relevance, ads, fraud and credit decisioning. The LLM content in these roles is frequently a paragraph bolted onto a job that is otherwise unchanged since 2022 — which is not a criticism, because this is where a great deal of measurable business value still sits, but it does change how you should prepare.
Titles this hides behind
Note the "Applied Scientist" convention — an Amazon lineage, and in India it very often means ranking and recommendations specifically rather than research.
Who is hiring it
India is the stronghold: Meesho, InMobi, Glance, PhonePe, Flipkart, Swiggy, Navi, Slice. In the US: Reddit, Pinterest, DoorDash, Instacart, Robinhood, Coinbase. Recommendations and ranking appear in 38% of Indian AI JDs against a far smaller share of US ones — the clearest geographic difference in the entire dataset.
How to recognise it in a JD
Look for ranking, personalisation, relevance,
CTR, A/B, feature store, Spark,
underwriting. If Spark and SQL appear near the top, this is archetype 3
whatever the title promises.
The loop
Prepare like this
- Own one end-to-end story with real numbers — baseline, change, measured lift, and how you knew it was the change rather than seasonality. Vague numbers read as borrowed credit.
- Be sharp on the offline-online gap, because it is the round most candidates lose. Training-serving skew, feedback loops, position bias in logged data.
- Separate model metrics from business metrics out loud. NDCG went up and revenue did not — be able to explain how that happens and what you do next.
- Refresh the classical material. This is the one archetype where gradient boosting, calibration and feature engineering still carry an interview.
Be ready for
- "Offline AUC improved, the A/B was flat. What are your hypotheses, in order?" The signature question of this archetype.
- "Your training data comes from what your current model showed users. What is the problem?" Position and exposure bias — and whether you have thought about counterfactual logging.
- "How often do you retrain, and how would you know that is right?"
Red flags in the posting
An LLM paragraph with no LLM substance. If "GenAI" appears once in the responsibilities and never again in the requirements, expect the actual work to be gradient boosting on tabular data. Fine if you want that — costly if you took the role to move into LLM work.
No mention of experimentation. A ranking team without A/B infrastructure cannot tell you whether your work mattered, and neither can your promotion packet.
Compensation
What this role tests
Themes, and where to learn them
These chapters are shared across every role that tests them, so preparation here compounds rather than being spent once.
Practice questions across all themes → · Back to Artificial Intelligence →