Amgen AI Research Scientist interview typically runs 2 rounds: hiring manager/team lead and a group interview with a senior member. It usually takes about 1-2 weeks and is fairly conversational, with some technical discussion.
$135K
Avg. Base Comp
$164K
Avg. Total Comp
2
Typical Rounds
1-2 weeks
Process Length
Our candidates report that Amgen’s AI Research Scientist interviews are less about polished “interview performance” and more about whether you can defend your research thinking under mild pressure. The strongest signal in the experience we saw was the emphasis on PhD-level depth: even when questions drifted away from the exact job description, the interviewer still pushed on fundamentals and expected the candidate to explain their work clearly, including a conceptual question about quantifying mixing. That tells us Amgen is looking for someone who can translate scientific judgment into plain language, not just someone who can name methods.
A recurring theme is that the process can feel uneven depending on who is in the room. The team lead came across as warm and straightforward, while the larger panel felt more awkward because one senior interviewer seemed disconnected from the candidate’s background and kept steering into broader scientific territory. We’ve seen that kind of mismatch matter here: candidates do best when they can stay composed, connect answers back to their own research, and avoid sounding overly scripted. In other words, Amgen seems to value scientific credibility plus calm communication more than flashy technical breadth.
Synthesized from 1 candidate report by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Amgen process.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amgen
What do you tell an interviewer when they ask you what your strengths and weaknesses are?
| Question | |
|---|---|
| 2nd Highest Salary | |
| Experiment Validity | |
| Hurdles In Data Projects | |
| P-value to a Layman | |
| Weighted Keys | |
| Valid Anagram | |
| Reducing Error Margin | |
| RMS Error | |
| Fair Coin | |
| Random Forest Explanation | |
| 85% vs 82% | |
| Greatest Common Denominator | |
| Softmax vs Logistic | |
| Possible Triangles | |
| Unbiased Estimator | |
| Slow SQL Query | |
| Sum to Zero | |
| Secret Wins | |
| Missing Housing Data | |
| Flatten JSON | |
| Overfit Avoidance | |
| String Palindromes | |
| Loan Model | |
| Batch vs Mini-Batch vs Stochastic Gradient Descent | |
| Digit Accumulator | |
| Addressing Data Quality Issues | |
| Search Linked List | |
| Client Solution Pushback | |
| Common Prefix |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with a conversational one-on-one with the team lead. This round is mostly behavioral and role-alignment focused, including a basic discussion of your background and making sure you understand the contract terms for the position.
The second round is a larger group interview with the team lead plus senior team members. Questions are mostly fair and tied to your PhD and research experience, but one senior interviewer may push into broader scientific topics and ask conceptual questions such as how you quantify mixing.
A major theme of the process is how clearly you can talk through your PhD work and defend the basics of your research. Interviewers may drift beyond the exact job description, so being able to explain your methods, reasoning, and scientific judgment matters as much as answering the specific question.