ChatGPT seems to be everywhere these days. With the recent announcement of its latest version, v4, I decided to put its reasoning capabilities to the test, specifically focusing on engineering management.
To accomplish this, I presented ChatGPT with a series of engineering manager interview questions. To make the experiment more engaging, I asked multiple follow-up questions, just like an interviewer would in a real interview setting.
One thing to note is that ChatGPT tends to generate rather lengthy responses. You can either check out the questions and answers linked below or jump right to my findings! Now, let's dive right in!
(ChatGPT prompt: "You are an engineering manager at Google. Tell me an example when you developed and retained a team member")
As an engineering manager at Google, I once faced a situation where one of my top-performing team members, Emily, was considering leaving the company for a new opportunity. To develop and retain her within the team, I took the following steps:
One-on-one meeting: I scheduled a one-on-one meeting with Emily to understand her reasons for considering a departure, her career aspirations, and any concerns she might have had. During the conversation, Emily shared that she was seeking new challenges and growth opportunities.
Personalized development plan: Based on Emily's feedback, I worked with her to create a personalized development plan that addressed her desire for growth and new challenges. We identified areas where she could expand her skills, such as taking on a more prominent role in a high-impact project or leading a small sub-team.
Cross-functional collaboration: To provide Emily with a broader perspective and diverse experiences, I facilitated her involvement in cross-functional projects. This allowed her to work with colleagues from different departments and learn about various aspects of the business, ultimately expanding her skill set.
Training and education: I encouraged Emily to attend industry conferences, workshops, and training sessions relevant to her career goals. Additionally, I arranged for her to participate in internal leadership training programs to further develop her managerial and interpersonal skills.
Recognition and rewards: To acknowledge Emily's contributions and dedication to the team, I ensured she received appropriate recognition and rewards, including a promotion and a salary increase. This demonstrated the company's appreciation for her hard work and commitment.
Regular follow-ups: I maintained an ongoing dialogue with Emily through regular one-on-one meetings, discussing her progress, addressing any concerns, and adjusting her development plan as needed.
These efforts led to Emily feeling more engaged, challenged, and valued within the team. She ultimately decided to stay with the company and continued to make significant contributions to our projects. This experience demonstrated the importance of understanding individual team members' needs and providing tailored development opportunities to foster growth, satisfaction, and retention.
(ChatGPT prompt: "You are an engineering manager at Apple. Tell me about a time you had a conflict with your manager and how you resolved it")
As an engineering manager at Apple, I once had a conflict with my manager over the prioritization of certain features in a new product release. My manager insisted on prioritizing features based on market trends and potential revenue, while I believed that focusing on user experience and long-term customer satisfaction was more crucial. This disagreement led to tension between us and had the potential to impact the project's direction.
To resolve the conflict, I took the following steps:
Self-reflection: I started by reflecting on my own perspective and the reasons behind my disagreement with my manager. I considered the validity of both our viewpoints and acknowledged that we both had the best interests of the project in mind.
Request a meeting: I approached my manager and requested a private meeting to discuss our differing opinions on the prioritization of features. I emphasized that I valued their perspective and believed that finding a common ground would be in the best interest of the project.
Open communication: During the meeting, I presented my viewpoint on the importance of user experience and long-term customer satisfaction. I shared examples and data to support my perspective, while also actively listening to my manager's reasoning behind their prioritization approach.
Seek understanding: I made an effort to understand my manager's viewpoint and the rationale behind their prioritization. I asked questions and sought clarification to ensure I fully grasped their perspective.
Find a compromise: After discussing both of our viewpoints, we worked together to find a compromise that balanced market trends, potential revenue, user experience, and long-term customer satisfaction. We agreed on a hybrid prioritization approach that took all these factors into account.
Develop a plan: With a compromise in place, we collaboratively developed a plan to prioritize and implement the selected features for the new product release. We established clear goals, deadlines, and responsibilities to ensure the project's success.
Maintain open dialogue: Following the resolution, I continued to maintain open communication with my manager, providing updates on the project's progress and discussing any concerns or new ideas.
By addressing the conflict in a proactive, respectful, and solution-focused manner, we were able to resolve our disagreement and develop a more effective strategy for the project. The experience highlighted the importance of open communication, understanding, and collaboration in resolving conflicts and fostering a positive working relationship.
After discussing both of our viewpoints, my manager and I agreed on a hybrid prioritization strategy that balanced market trends, potential revenue, user experience, and long-term customer satisfaction. This approach aimed to address the concerns from both perspectives and ensure a successful product release.
(ChatGPT prompt: "You are an engineering manager at Facebook. How do you manage through hard times? Keep it short.")
As an engineering manager at Facebook, managing through hard times involves:
(ChatGPT prompt: "You are an engineering manager at Netflix. Tell me in detail about the architecture of a project you've been involved with.")
As an engineering manager at Netflix, I was responsible for overseeing the development and implementation of a highly-scalable and resilient recommendation engine. This system was designed to provide personalized content suggestions to millions of users based on their viewing history, preferences, and other user behavior data. Here is a detailed overview of the project's architecture:
Microservices architecture: We employed a microservices architecture to ensure that each component of the recommendation engine was developed, deployed, and maintained independently. This approach provided flexibility, scalability, and fault tolerance, allowing individual components to be updated or replaced without affecting the overall system.
Data ingestion: User behavior data, such as watch history, ratings, and browsing patterns, were collected through various touchpoints in the Netflix application. This data was then streamed in real-time using Apache Kafka, which served as a highly-scalable and fault-tolerant messaging system.
Data storage: We utilized Apache Cassandra, a highly-scalable and distributed NoSQL database, for storing user behavior data. This choice allowed us to handle massive amounts of data while providing low-latency read and write operations. Additionally, we used Amazon S3 for storing large volumes of metadata related to movies, TV shows, and user profiles.
Data processing: The collected data was processed using Apache Spark, a distributed computing framework that provided fast and scalable data processing capabilities. The processed data was used to generate user-specific features and train machine learning models.
Machine learning models: We employed a variety of machine learning algorithms, such as collaborative filtering, content-based filtering, and deep learning techniques, to generate personalized recommendations for users. These models were trained and updated periodically based on the latest user behavior data.
Recommendation API: The recommendation engine was exposed through a RESTful API, which allowed the Netflix application to request personalized content suggestions for users. The API was built using a lightweight and high-performance web framework like Flask, ensuring low-latency responses and high throughput.
Load balancing and caching: We utilized load balancers, such as NGINX or HAProxy, to distribute incoming traffic evenly across multiple instances of the recommendation API. This ensured optimal performance and fault tolerance. Additionally, we employed caching mechanisms like Redis to store frequently accessed recommendations, reducing the load on the recommendation engine and improving response times.
Monitoring and observability: We incorporated monitoring tools like Prometheus and Grafana to track the performance and health of the recommendation engine. This allowed us to identify and resolve issues proactively, ensuring the system's reliability and stability.
By implementing this robust architecture, we successfully developed a highly-scalable and resilient recommendation engine that provided personalized content suggestions to millions of Netflix users, enhancing their viewing experience and driving user engagement.
To manage the project timeline, I:
(ChatGPT prompt: "How would you design a system that counts the number of clicks on YouTube videos? Ask me clarifying questions if needed.")
To design a system that counts the number of clicks on YouTube videos, I'd like to ask a few clarifying questions:
Based on your requirements, here's a design for a system that counts the number of unique clicks on YouTube videos for video playback:
Data collection: When a click event occurs, send a message with the video ID, user ID, timestamp, and other relevant metadata to a backend service for processing. Use a high-throughput, low-latency messaging system like Apache Kafka or Google Pub/Sub to handle the incoming messages.
Data processing: Implement a stream processing system, such as Apache Flink or Apache Beam, to consume and process click events. Perform tasks like data validation, enrichment, deduplication (to ensure unique clicks), and aggregation based on video ID.
Data storage: Store the aggregated unique click counts in a scalable and distributed database like Apache Cassandra or Google Bigtable. This will allow you to handle the growth of YouTube while providing low-latency read and write operations. Additionally, store the raw click events in a distributed data storage system like Apache Hadoop HDFS or Google Cloud Storage for historical analysis and future processing needs.
Batch processing for historical data: Use a batch processing framework like Apache Spark or Hadoop MapReduce to periodically process the raw click events stored in the distributed data storage. This can be used to generate historical reports, detect trends, and perform other analytical tasks.
Monitoring and alerting: Set up monitoring and alerting tools like Prometheus and Grafana to track the performance and health of the system. This will help you proactively identify and resolve issues, ensuring the reliability and stability of the system.
Accessing click count data: Develop an internal API or dashboard that allows authorized users to query the unique click count data stored in the database. This can be used for reporting, analysis, and decision-making purposes.
By implementing this design, you can create a scalable, reliable, and efficient system that counts the number of unique clicks on YouTube videos for video playback and stores historical data for analysis and reporting.
Testing ChatGPT's abilities on the topic of managing people turned out to be a entertaining experiment. Here are my key takeaways:
So, did ChatGPT get the job? Not this time (but it's welcome to reapply in six months).