InstaDeep interview process & guide 2026
Everything we know about interviewing at InstaDeep: the process stage by stage, what each round tests, and compensation by level.
- 1Initial Screening
- 2Technical Interviews and Technical Assessment
- 3Coding Challenges and/or Take-home Assignments
- 4Behavioral and Cultural Fit
- 5Debrief, HR Discussions, and Final Round
Interviewing at InstaDeep
InstaDeep’s interviews are heavily research and ML oriented, with problem solving and software fundamentals showing up alongside domain depth. Across the reported steps, you should expect a mix of technical interviews, coding and technical assessments, and some explicit soft skills and cultural fit evaluation.
The topics data shows the core of what you are likely tested on: Machine Learning (general) and Machine Learning Fundamentals are both very prominent, Deep Learning is also very prominent, and RL, Graph Neural Networks, and Python appear frequently. On the engineering side, Python, OOP, and Software Engineering Fundamentals show up, and there is a recurring evaluation of problem solving.
What happens after you finish the interviews is only partially described in the step list, but there is a reported debrief session that clarifies performance on the technical assessment. The candidate reports you provided show a difficulty distribution skewed toward medium and hard questions, but the aggregated offer rate in these reports is 0.0%, and positive sentiment is 28.7%, so you should treat this as a high-bar process and prepare accordingly.
Figma appears as a technical assessment topic with the maximum percentile in the topic list, so if you are interviewing for a role that touches design (for example UX/UI), plan to be ready to demonstrate design-tool proficiency, not just general UX knowledge.
How hard is the InstaDeep interview?
Aggregated from 101 interview experiencesAbout 1 in 6 candidates with a known outcome convert.
The interview process, end to end
5 rounds · based on 101 candidate reports- 1Initial Screening
You start with an initial screening focused on evaluating your qualifications and fit for the role. Some loops also report a separate HR screening call for UX/UI, indicating early-stage screening can be role-specific.
- 2Technical Interviews and Technical Assessment
You complete a series of technical interviews that assess theoretical knowledge and practical applications, including problem solving, system design discussions, and domain knowledge. Separately, a technical assessment step is reported that includes reinforcement learning, deep learning, and machine learning concepts, and in some cases evaluates design-tool proficiency, particularly Figma.
- 3Coding Challenges and/or Take-home Assignments
Some loops include coding challenges to test algorithmic thinking and problem solving, and a take-home assignment is also reported as part of technical evaluation. Treat this as part of the technical proof step, not just a formality.
- 4Behavioral and Cultural Fit
You may go through behavioral interviews focused on communication skills, teamwork, and soft skills, plus a cultural fit evaluation centered on collaboration style. This is where you should align your examples with teamwork and how you operate in a group.
- 5Debrief, HR Discussions, and Final Round
A debrief session is reported that discusses performance in the technical assessment and clarifies misunderstandings. After that, there are reported HR discussions and final round discussions with potential colleagues to assess technical skills and interpersonal compatibility.
What InstaDeep actually tests for
How prominent each skill is across reported loopsFind the guide for your role
This is your next step: open the guide for the role you are interviewing for. Each one carries the questions InstaDeep interviewers actually ask that position, the loop structure, and pay by level.
What InstaDeep pays, by level
Estimated total compensation: base salary plus stock and annual cash bonus.
What separates offers from rejections
Patterns from candidates who got offers, and the mistakes that most often sink a loop.
Do this
- Prioritize Machine Learning fundamentals and Deep Learning concepts, because both are very prominent in the topic data, and they show up alongside broader ML topics in the reported assessment themes.
- Be ready to discuss RL and Graph Neural Networks (GNNs) at a concept and application level, since both appear as prominent technical topics in the extracted interview-question data.
- Brush up on Python and OOP, and practice writing clear solutions under time pressure, because Python is highly prominent and OOP and software engineering fundamentals are also present.
- If your loop includes a design evaluation, practice using Figma for your relevant task, since Figma is explicitly listed as a technical assessment focus.
Avoid this
- Do not underestimate “problem solving” and communication, because problem solving is marked as very prominent and there are reported behavioral and cultural fit stages focused on soft skills.
- Avoid relying only on ML theory, because the reported technical interviews include coding assessments and system design discussions, and there is also a reported coding-challenge step and take-home assignments.
- Do not ignore reinforcement learning and deep learning, because they appear as explicit topics in the assessment-focused step descriptions, not just as general ML background.
- Do not assume a single interview type is enough, because the process description includes multiple technical interview formats, plus screening, debrief, and HR discussions in some reported loops.
InstaDeep interview FAQ
Answered from real candidate and workplace dataHow hard is the interview process?
In the candidate reports you provided, difficulty is split across easy 13.9%, medium 60.4%, hard 20.8%, and very hard 5.0%. That distribution suggests most questions are medium, but a substantial portion are hard and you should expect at least some very challenging moments.
Is there an offer after the interviews?
In the aggregated candidate reports you provided, the offer rate is 0.0%. The reported process steps include debrief, HR discussions, and final round discussions, but the dataset summary you provided does not show offers being made.
What topics should I prioritize most?
From the extracted question data, prioritize Machine Learning (general) and Machine Learning Fundamentals, then Deep Learning and Python. RL, GNNs, and Deep Learning are also prominent, and problem solving appears as very prominent across interviews.
Do they do coding or take-home work?
Yes. The reported steps include coding assessments and system design discussions inside technical interviews, plus a reported coding-challenge step and a reported take-home assignment step. You should plan to demonstrate both algorithmic thinking and practical implementation.
Is Figma part of the process?
Yes, Figma is explicitly listed as a technical assessment topic with the maximum percentile in the topic data. The reported technical assessment step also mentions evaluating proficiency in design tools, so if you are interviewing for a design-adjacent role, treat Figma practice as part of your prep.
Can I reapply if I do not pass?
Your provided data does not include any policy about reapplication or retry timelines. If you want to know this for certain, you would need to confirm with the recruiting team directly.
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