One Machine Learning Scientist's account of interviewing for an AI research role at AbbVie: recruiter and HR screens, a hiring manager screen, a panel centered on publications, and a one-hour research presentation.
$128K
Avg. Base Comp
$160K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
AbbVie's AI Research Scientist loop, as one Machine Learning Scientist described it, leans toward research conversation rather than coding exercises. That candidate began with a recruiter call and two HR screens, then met a hiring manager to talk through their educational background. A panel followed with only minimal technical questions and spent most of its time on publications and prior research. The final step was a one-hour presentation to a large group, which they called the most intense part because the questions showed people had read their work closely. This is a single account, so treat it as one example rather than a fixed sequence.
The technical questions were high level: how you train on large-scale data and how you handle batch effects. Expect the emphasis to fall on research judgment and practical ML experience rather than algorithm puzzles. There was also a behavioral question about communicating something difficult to a teammate, including how the conversation went and what happened afterward.
To prepare, know your papers well enough to defend each design choice, and practice a talk that holds up under detailed questions. Have a plain-language explanation of large-scale training and batch effects, plus one short story about a hard conversation with a teammate. The role in this report was a contract position, so it is worth clarifying compensation early in the process.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Abbvie process.
The process felt more research-focused than deeply technical, which was a little surprising for an AI Research Scientist role. I first had a recruiter call, then two HR screens that were mostly about getting to know me as a person. After that I had a hiring manager screen where we talked through my educational background, followed by a panel interview that had only minimal technical questions and spent most of the time on my publications and prior research. The last step was a one-hour presentation with a lot of attendees, and that was the most intense part of the process because the questions were thoughtful and clearly came from people who had read my work closely.
The technical questions themselves were fairly high level rather than algorithmic. I was asked how I train large-scale data and how I deal with batch effects, so the emphasis was on research judgment and practical ML experience instead of coding puzzles. There was also a behavioral question about a time I had to communicate something difficult to a teammate, and they wanted to hear how I handled the conversation and what happened afterward. Overall the interviews were well planned and professional, but the compensation for the contract opportunity was below market and even seemed to be reduced further when affordability concerns came up, which made the process feel less transparent. I ultimately declined the offer, and after that I was not considered for future opportunities. My main takeaway is to be ready to walk through your research clearly, explain your approach to large-scale training and batch-effect issues, and pay close attention to how compensation is handled early.
Prep tip from this candidate
Be ready to present your publications clearly and answer practical research questions like how you train on large-scale data and how you handle batch effects. Also, expect at least one behavioral question about difficult team communication, so have a concise example ready.
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Topics based on recent interview experiences.
Featured question at Abbvie
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Synthesized from candidate reports. Individual experiences may vary.
The Machine Learning Scientist candidate started with a recruiter call, followed by two HR screens that were mostly about getting to know them as a person rather than testing technical skills. Be ready to explain your background and motivation clearly, and ask about compensation early.
The hiring manager screen in that report walked through the candidate's educational background. The panel that followed had minimal technical questions and focused on publications and prior research, with a high-level question on training with large-scale data and handling batch effects, plus one behavioral question about a difficult conversation with a teammate.
The last step was a one-hour presentation with many attendees. The candidate found it the most intense stage because the questions were thoughtful and came from people who had read their work closely. Rehearse a talk that can hold up under detailed follow-ups on your methods and results.
