Ho presentato la mia candidatura online. Ho sostenuto un colloquio presso Lucid Motors nel mese di giu 2023
Colloquio difficile
Candidatura
Ho presentato la mia candidatura online. Ho sostenuto un colloquio presso Lucid Motors
Colloquio
Phone Screening _ Intro round between HR and you which goes for about 25 - 30 mins
Tech Round: Python and SQL Coding
Panel Round: Case Study related to data
HR Screening: Final Negotiations between you and Comapny
Domande di colloquio [1]
Domanda 1
My Experience level and what interested me about the Company
Applied online, no referral. Signed an NDA so no specific details provided below but rather a high-level overview. 1st round was a typical HR screen "tell me about yourself, what you're looking for etc". Asked if I'd be comfortable with RTO 5 days/week at their Newark office.
2nd round was a tech screen with a few different parts:
1) LC style coding - first question was a basic hashmap question, next question was a tricky variant of two-sum.
2) ML theory - refresh your fundamentals on loss functions, metrics, statistical tests / concepts (ex. independence)
3) Resume deep-dive.
I moved on to the on-site, but I was never told what to expect for each round even though I asked 3 times over a couple of weeks. Very, very unprofessional. but sadly a common experience these days in the interview process. I withdrew from the process as I already received an offer.
Domande di colloquio [1]
Domanda 1
hashmap question, two sum variant, ML theory questions
Ho presentato la mia candidatura online. La procedura ha richiesto 4 settimane. Ho sostenuto un colloquio presso Lucid Motors nel mese di gen 2023
Colloquio
Very well defined interviews, assessing your skills across, coding, design, ML deep dive and behavioral. Make sure to prepare for ML design questions well. Not a typical design problem. But rather ones you can expect from automotive field e.g. based on telemetry data etc.
Domande di colloquio [1]
Domanda 1
Describe the difference between various bagging and boosting methods in terms of bias and variance trade-off