AI/ML Software Engineer Interview Experience at Coda

Having interviewed for the AI/ML Software Engineer position at Coda, I can share insights into the interview process, the types of questions asked, and the skills that were emphasized. Here’s a breakdown of what to expect and how to prepare for the role:

Interview Process Overview:

Initial Screening (Phone Interview with Recruiter)

The process started with a phone screening conducted by a recruiter. This was primarily focused on my background and experience, and whether I was a good fit for the AI/ML role at Coda. The recruiter asked questions such as:

• Tell me about your experience with machine learning projects.
• What are your thoughts on working in a collaborative environment like Coda’s?
• How do you approach learning and implementing new technologies in your projects?

The recruiter also provided an overview of Coda’s mission and how AI/ML fits into the company’s product development, including their work with large language models (LLMs) and natural language processing (NLP).

Technical Interview (Zoom with Hiring Manager)

The second round involved a technical interview with the hiring manager. This was a more in-depth discussion about my experience with machine learning and the tools I have worked with. They were particularly interested in my hands-on experience with end-to-end model development, including training, evaluation, and deployment. Some example questions included:

• Can you describe an ML model you’ve developed from scratch, including data collection, preprocessing, and model evaluation?
• Have you worked with large language models (LLMs)? What challenges did you face and how did you overcome them?
• How do you approach optimizing a model for better performance?

I was also asked to explain how I have worked with frameworks like TensorFlow and PyTorch in previous projects. The hiring manager was keen to hear about specific projects where I had successfully implemented ML solutions, particularly those that were deployed into production.

Coding and Algorithm Challenge

In this round, I was given a coding challenge to assess my programming and problem-solving skills. The task involved writing code for an ML problem, which I needed to solve within an hour. Some typical problems might include:

• Implementing a basic neural network for classification tasks using PyTorch or TensorFlow.
• Optimizing a given function or model using hyperparameter tuning techniques.
• Solving algorithmic problems related to data manipulation, feature engineering, or model evaluation.

I was expected to explain my thought process as I solved the challenge, including any trade-offs I considered while implementing the solution.

On-site Interview (Virtual)

The final round was a virtual onsite where I met with several team members, including senior engineers, AI experts, and product managers. This was a collaborative session where we worked through real-world scenarios related to AI/ML in the context of Coda’s product. The team wanted to see how I would approach solving complex AI/ML problems and working within their existing architecture.

During the session, I was presented with a case where Coda wanted to integrate an AI feature to enhance user-generated content. I was asked to:

• Design a system using LLMs that would allow users to generate and summarize content automatically.
• Discuss the technical stack and algorithms I would use for building and deploying the model.

The team was also interested in how I would prioritize tasks, manage resources, and collaborate across different teams, as Coda’s AI initiatives span multiple product surfaces.

Behavioral Interview

In addition to the technical rounds, I participated in a behavioral interview to assess my cultural fit and teamwork skills. Questions included:

• Tell me about a time when you had to work under pressure to deliver an ML model on time. How did you handle it?
• Describe a situation where you had to collaborate with non-technical team members to explain the capabilities of an AI model.
• How do you stay motivated when working on a long-term project or facing challenges in the development process?

The behavioral interview aimed to understand my communication skills, ability to adapt in a fast-paced environment, and how well I could work within Coda’s mission of empowering users with AI-driven tools.

Key Skills and Qualities Evaluated:

• Machine Learning Expertise: The primary focus was on my ability to develop, train, and deploy machine learning models. Experience with frameworks like TensorFlow, PyTorch, and familiarity with large language models were essential.
• Problem Solving and Coding: My ability to solve algorithmic problems quickly and efficiently was tested through coding challenges.
• Collaboration: Given the collaborative nature of the role, they assessed how I would work with product managers and other engineers to implement AI features and solve real-world problems.
• Adaptability: As the AI field is rapidly evolving, Coda wanted to ensure I could learn new technologies and adapt to their ever-changing product needs.
• Communication: Explaining complex AI concepts to both technical and non-technical stakeholders was a key evaluation criterion. I was expected to demonstrate clarity in communication and the ability to simplify technical ideas.

Example from My Interview:

One of the most challenging parts of the interview was when I was asked to design a system that integrates AI to enhance user-generated content. I proposed using a fine-tuned GPT-3 model to generate summaries and extract key points from content. I described the process of training the model with Coda’s proprietary data, fine-tuning it for summarization tasks, and using a feedback loop from user interactions to improve the model over time. I explained how I would use APIs to integrate this AI service into Coda’s workflow, ensuring that the system could scale and provide value to users. The team appreciated my detailed thought process and the technical feasibility of my proposal.