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AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. Deep learning skills are sometimes required, especially in organizations focusing on computer vision, natural language processing, or speech recognition.

The deep learning case study interview focuses on technical and decision making skills, and you’ll encounter it during an onsite round for a Deep Learning Engineer (DLE), Deep Learning Researcher (DLR), or Software Engineer-Deep Learning (SE-DL) role. You can learn more about these roles in our AI Career Pathways report and about other types of interviews in The Skills Boost.

I   What to expect in the deep learning case study interview

The interviewer is evaluating your approach to a real-world deep learning problem. The interview is usually a technical discussion on an open-ended question. There is no exact solution to the question; it’s your thought process that the interviewer is evaluating. Here’s a list of interview questions you might be asked:

II   Recommended framework

All interviews are different, but the ASPER framework is applicable to a variety of case studies:

  1. Ask. Ask questions to uncover details that were kept hidden by the interviewer. Specifically, you want to answer the following questions: “what are the product requirements and evaluation metrics?”, “what data do I have access to?”, ”how much time and computational resources do I have to run experiments?”, ”how will the learning algorithm be used at test time, and does it need to be regularly re-trained?”.
  2. Suppose. Make justified assumptions to simplify the problem. Examples of assumptions are: “we are in small data regime”, “the data distribution won’t change over time”, “our model performs better than humans”, “labels are reliable”, etc.
  3. Plan. Break down the problem into tasks. A common task sequence in the deep learning case study interview is: (i) data engineering, (ii) modeling, and (iii) deployment.
  4. Execute. Announce your plan, and tackle the tasks one by one. In this step, the interviewer might ask you to write code or explain the maths behind your proposed method.
  5. Recap. At the end of the interview, summarize your answer and mention the tools and frameworks you would use to perform the work. It is also a good time to express your ideas on how the problem can be extended.

III   Interview tips

Every interview is an opportunity to show your skills and motivation for the role. Thus, it is important to prepare in advance. Here are useful rules of thumb to follow:

Show your motivation.

In deep learning case study interviews, the interviewer will evaluate your excitement for the company’s product. Make sure to show your curiosity, creativity and enthusiasm.

Listen to the hints given by your interviewer.

Example: You’re asked to automatically identify words indicating a location in science fiction books. You decide to use word2vec word embeddings. If your interviewer asks you “how were the word2vec embeddings created?”, she is digging into your understanding of word2vec and might be expecting you to question your choice. Seize this opportunity to display your mastery of the word2vec algorithm, and to ask a clarifying question. In fact, maybe the data distribution in the science fiction books is very different from the data distribution of the text corpora used to train word2vec. Maybe the interviewer is expecting you to say “although it will require significant amounts of data, we could train our own word embeddings on science fiction books.”

Show that you understand the development life cycle of an AI project.

Many candidates are only interested in what model they will use and how to train it. Remember that developing AI projects involves multiple tasks including data engineering, modeling, deployment, business analysis, and AI infrastructure.

Avoid clear-cut statements.

Because case studies are often open-ended and can have multiple valid solutions, avoid making categorical statements such as “the correct approach is …” You might offend the interviewer if the approach they are using is different from what you describe. It’s also better to show your flexibility with and understanding of the pros and cons of different approaches.

Study topics relevant to the company.

Deep learning case studies are often inspired by in-house projects. If the team is working on a domain-specific application, explore the literature.

Example 1: If the team is building an automatic speech recognition (ASR) software, review popular speech papers such as Deep Speech 2 (Amodei et al., 2015), audio datasets like Librispeech (Panayotov et al., 2015), as well as evaluation metrics like word error rate used to evaluate speech models.

Example 2: If the team is working on a face verification product, review the face recognition lessons of the Coursera Deep Learning Specialization (Course 4), as well as the DeepFace (Taigman et al., 2014) and FaceNet (Schroff et al., 2015) papers prior to the onsite.

Example 3: If you’re interviewing with the perception team of a company building autonomous vehicles, you might want to read about topics such as object detection, path planning, safety, or edge deployment.

Articulate your thoughts in a compelling narrative.

Your interviewer will often judge the clarity of your thought process, your scientific rigor, and how comfortable you are using technical vocabulary.

Example 1: When explaining how a convolution layer works, your interviewer will notice if you say “filter” when you actually meant “feature map”.

Example 2: Mispronouncing a widely used technical word or acronym such as NER, MNIST, or CIFAR can affect your credibility. For instance, MNIST is pronounced “ɛm nist” rather than letter by letter.

Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Don’t mention methods you’re not able to explain.

Example: If you mention batch normalization, you can expect the interviewer to ask: “could you explain batch normalization?”.

Write clearly, draw charts, and introduce a notation if necessary.

The interviewer will judge the clarity of your thought process and your scientific rigor.

Example: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

When you are not sure of your answer, be honest and say so.

Interviewers value honesty and penalize bluffing far more than lack of knowledge.

When out of ideas or stuck, think out loud rather than staying silent.

Talking through your thought process will help the interviewer correct you and point you in the right direction.

IV   Resources

You can build AI decision making skills by reading deep learning war stories and exposing yourself to projects. Here’s a list of useful resources to prepare for the deep learning case study interview.

Deep learning engineers carry out data engineering, modeling, and deployment tasks. They demonstrate solid scientific and engineering skills. Communication skills requirements vary among teams. This role is a variant of machine learning engineer. It requires deep learning skills in addition to the skills profile presented in the figure above.

Deep learning researchers carry out data engineering and modeling tasks. They demonstrate outstanding scientific skills (see Figure above). Communication skills requirements vary among teams. This role is a variant of machine learning researcher. It requires deep learning skills in addition to the skills profile presented in the figure above.

People who have the title software engineer-machine learning carry out data engineering, modeling, deployment and AI infrastructure tasks. They demonstrate solid engineering skills and are developing scientific skills. Communication skills requirements vary among teams. This role is a variant of software engineer-machine learning. It requires deep learning skills in addition to the skills profile presented in the figure above.

Developing an AI project development life cycle involves five distinct$:$ data engineering, modeling, deployment, business analysis, and AI infrastructure.

A convolution layer's filter is a set of trainable parameters that convolves across the convolution layer's input.

A feature map is one channel of a convolution layer's output. It results from convolving a filter on the input of a convolution layer.

In natural language processing, NER refers to Named Entity Recognition. It is the task of locating and classifying named entity (e.g., Yann Lecun, Trinidad and Tobago, and Dragon Ball Z) in text into pre-defined categories such as person names, organizations, locations, etc.

Batch normalization is a technique for improving the speed, performance, and stability of artificial neural networks. It was introduced by Ioffe et al. in 2015. (Wikipedia)

Medical imaging technologies are extremely varied, and used in many medical disciplines including radiology, dermatology, pathology, and cardiology. For example, CheXNet (Rajpurkar, Irvin, et al.) is a deep learning model that aims to support practicing radiologists in detecting pneumonia from chest X-rays.

Author(s)

  1. Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at deeplearning.ai

Acknowledgment(s)

  1. The layout for this article was originally designed and implemented by Jingru Guo, Daniel Kunin, and Kian Katanforoosh for the deeplearning.ai AI Notes, and inspired by Distill.

Footnote(s)

  1. Job applicants are subject to anywhere from 3 to 8 interviews depending on the company, team, and role. You can learn more about the types of AI interviews in The Skills Boost. This includes the machine learning algorithms interview, the deep learning algorithms interview, the machine learning case study interview, the deep learning case study interview, the data science case study interview, and more coming soon.
  2. It takes time and effort to acquire acumen in a particular domain. You can develop your acumen by regularly reading research papers, articles, and tutorials. Twitter, Medium, and machine learning conferences (e.g., NeurIPS, ICML, CVPR, and the like) are good places to read the latest releases. You can also find a list of hundreds of Stanford students' projects on the Stanford CS230 website.
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