Graphcore Research Scientist interview questions & guide 2026

The following questions are representative of the patterns observed in recent Graphcore interview experiences. While specific topics may shift based on the current research focus of the team, the emphasis remains consistently on your depth of understanding in mathematics, machine learning theory, and coding proficiency.

Technical / Domain Knowledge

These questions probe your grasp of fundamental concepts in linear algebra, probability, and deep learning mechanics.

• Which vector belongs to the null space of this specific matrix?
• How would you explain the process of SVD (Singular Value Decomposition) in the context of model compression?

Preparation for Graphcore requires a balanced approach. You must be as comfortable with a whiteboard or code editor as you are with a mathematical proof.

Technical Depth – You are expected to have a rock-solid foundation in linear algebra, calculus, and probability. Interviewers will move past surface-level definitions to test your ability to apply these concepts to real-world machine learning problems.

Hardware-Aware Thinking – Graphcore is unique because of its hardware. You should demonstrate an awareness of how your algorithmic choices impact computational efficiency, memory usage, and latency on specialized AI accelerators.

Interactive Problem Solving – During coding and case study rounds, your thought process is just as important as the final answer. Treat the interviewer as a collaborator; explain your trade-offs, why you chose a specific data structure, and how you would handle edge cases.

Communication of Research – You will likely be asked to discuss research papers or your own past projects. Be prepared to articulate the "why" behind your decisions, the limitations of your approach, and how you would iterate further.

The interview process at Graphcore is designed to be rigorous and fast-paced. You should expect a series of stages that evolve from high-volume technical screening to in-depth, team-oriented evaluations. The process typically begins with an automated assessment to filter for core mathematical and coding competencies, followed by a series of live interactions.

Candidates often report that the process is highly efficient, with quick turnaround times between stages. You will move from initial technical screens to more complex, interactive rounds involving live coding, paper discussions, and deep dives into your technical background. The company values candidates who can bridge the gap between abstract research and practical, performant implementations.

The timeline above illustrates the progression from initial screening to final-stage evaluations. Use this to pace your study; prioritize mastering the fundamental mathematics and coding patterns early, as these are the "gatekeeper" components. As you move into later stages, shift your focus toward synthesizing your research experience and demonstrating your ability to communicate complex ideas to a team of experts.

Mathematical Foundations

This area is non-negotiable. You will be evaluated on your ability to handle vectors, matrices, and statistical distributions with precision. Strong performance looks like a fluid ability to manipulate these structures and explain their role in machine learning models.

Be ready to go over:

• Linear algebra: Eigenvalues, null spaces, matrix rank, and SVD.
• Calculus: Derivatives and integrals as applied to gradient-based optimization.
• Probability: Sampling methods and distributions.

Example scenarios:

• Solving a matrix property question under time pressure.
• Deriving a gradient for a custom loss function.

Coding & Implementation

Beyond knowing the theory, you must be able to write clean, efficient code. You will be tested on your ability to translate mathematical concepts into working software that adheres to performance constraints.

Be ready to go over:

• Data structures and algorithmic complexity (Big O).
• Implementation of core ML primitives (e.g., attention, normalization).
• Tensor manipulation efficiency.

Example scenarios:

• Implementing a modified attention mechanism in a live coding environment.
• Optimizing a nested loop structure for better performance.

As a Research Scientist, you are responsible for bridging the gap between theoretical machine learning breakthroughs and their practical application on Graphcore’s hardware. You will spend your time researching new architectures, experimenting with model optimizations, and validating these approaches through rigorous benchmarks.

Collaboration is central to your success. You will work closely with hardware engineers to ensure that the models you develop are not just accurate, but also optimized for the specific architecture of the IPU. This involves constant feedback loops where you analyze performance metrics, identify bottlenecks, and iterate on your designs to squeeze maximum performance out of the system. You will also participate in team-wide discussions, contributing to the broader research roadmap and staying abreast of the latest developments in the field.

A strong candidate for this role is someone who combines the analytical mind of a researcher with the practical mindset of an engineer.

• Must-have skills:
  • Advanced proficiency in Linear Algebra, Probability, and Calculus.
  • Deep experience with Deep Learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
  • Strong coding skills in Python and/or C++.
  • Experience in implementing and debugging complex ML algorithms.
• Nice-to-have skills:
  • Familiarity with hardware-aware optimization or compiler-level knowledge.
  • Prior experience with robotics or specialized compute architectures.
  • A track record of high-quality research publications.

Q: How much time should I spend preparing? A: Given the technical depth required, most successful candidates spend several weeks reviewing core mathematics and practicing coding problems. Do not underestimate the need to be "exam-ready" for the initial HackerRank test.

Q: What is the most common reason for rejection? A: Candidates often fail when they rely too heavily on high-level ML knowledge while lacking the underlying mathematical rigor or coding speed required for the initial tests. Ensure your foundations in linear algebra are sharp.

Q: How do I stand out in the paper discussion round? A: Don’t just summarize the paper; provide a critical analysis. Discuss the trade-offs, suggest alternative approaches, and explain how the results would change if the underlying assumptions were altered.

Q: Is the coding interview language-specific? A: While Python is standard for research, the focus is on your problem-solving logic and algorithmic efficiency. Ensure you are comfortable expressing complex ideas in your chosen language without getting bogged down in syntax.

• Talk through your code: During live coding, silence is your enemy. Explain your thought process, why you are choosing a specific approach, and any trade-offs you are making.
• Review your resume: Be prepared to discuss every line of your research history in detail. If you mention a project, know the math and implementation details behind it inside and out.
• Practice under pressure: The initial online tests are timed. Use practice platforms to simulate the stress of a 20-minute, 20-question environment to build your speed.

The Research Scientist role at Graphcore offers a unique opportunity to shape the future of AI hardware and software. By focusing on your mathematical foundations, refining your coding speed, and developing a "hardware-aware" mindset, you will be well-positioned to succeed in this demanding process. You can explore additional interview insights, practice questions, and preparation resources on Dataford to further refine your strategy.

The compensation data provided reflects the total package typically associated with this level of seniority. Remember that compensation is often composed of base salary, performance bonuses, and equity, which can vary based on your specific experience and the location of the role. Use this data as a benchmark to ensure your expectations are aligned with the market for specialized research roles.