
Senior
Sr. MLE, Responsible AI at Pinterest — bias mitigation and Gen AI safety across a 550M-user recommender platform
This role sits on Pinterest's Advanced Technologies Group, driving fairness, safety, and alignment work across Gen AI evaluation, foundation model fine-tuning, and Red Teaming that touches Search, Homefeed, and Content Understanding. It calls for 4+ years shipping large-scale ML-driven products plus at least a year leading cross-team engineering efforts, with a Master's or PhD expected. Strong fits can speak concretely to fairness interventions they've deployed at scale and to leading horizontal initiatives across engineering orgs.
Practice this interview
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What this interview tests
- Bias detection and mitigation in production ML systems (fairness interventions at scale)
- Gen AI product evaluation, foundation model safety fine-tuning, and alignment
- Red Teaming methodology for generative models
- Transformer, two-tower, LLM/VLM architectures in large-scale search and recommender systems
- Cross-team technical leadership and driving Responsible AI adoption across engineering orgs
- Mentoring junior engineers on Responsible AI practices
Common question themes
Describe a fairness intervention you shipped and how you measured its impact against the primary product metric
How would you design a Red Teaming process for a new generative feature before launch
Walk through evaluating a foundation model for bias before fine-tuning it for a specific product surface
Tell me about a time you led a cross-team initiative without direct authority over the other teams
How do you balance fairness constraints against ranking quality in a large-scale recommender system
Describe mentoring a junior engineer through a Responsible AI problem
How candidates describe it
Real Sr. Machine Learning Engineer interview stories — retold from candidates' public write-ups, with sources.
Google · L5 Machine Learning EngineerOfferGoogle L5 machine learning engineer interview: phone screen skipped, four technical rounds, and an offer
A candidate applying for an L5 machine learning engineer role at Google had the phone screen skipped due to a referral and prior tenure at the company, then went through four technical and design rounds plus a behavioral round before receiving an L5 offer. The loop was one leg of a broader search that produced offers from several companies in the same cycle.
Interviewed April 2022 · Remote
Amazon · Applied Scientist (L4)OfferAmazon Applied Scientist Interview Experience: Alexa Speech Team, 2021
A redirected recruiter call turned into an Amazon Applied Scientist loop with the Alexa Speech team: a phone screen, a split five-round virtual onsite across two teams, a bar raiser, and an added ML-breadth round, ending in an offer with a downlevel from L5 to L4.
Interviewed July 2021 · Boston, MA (Remote)
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