1. What is an AI Engineer at Cognition?

As an AI Engineer at Cognition, you are at the absolute forefront of the generative AI revolution. We are the makers of Devin, the world’s first autonomous AI software engineer, and Windsurf, our AI-native IDE. In this role, you serve as the critical bridge between our cutting-edge agentic workflows and the enterprise engineering teams that rely on them. You are not just supporting a product; you are fundamentally changing how software is built by teaching, guiding, and enabling human engineers to collaborate with AI teammates.

The impact of this position is massive. Whether you lean toward the AI Enablement Engineer or AI Support Engineer track, your work directly influences product adoption, user retention, and the overall trajectory of our business. You will be working with development teams at thousands of enterprises—from agile startups to Fortune 500 companies—helping them integrate Devin into their day-to-day development, troubleshooting complex deployment architectures, and identifying high-ROI workflows across QA, support, and data teams.

This role requires a unique hybrid of deep technical expertise, consulting intuition, and an educator's mindset. Our founding team includes world-class competitive programmers and leaders from companies like DeepMind, Waymo, and Scale AI. We maintain an exceptionally high talent bar. You will be expected to pair-program alongside seasoned enterprise engineers, root-cause highly complex distributed systems issues, and build scalable playbooks that will train the next hundred thousand engineers worldwide.

2. Common Interview Questions

The questions below represent the types of challenges you will face during your Cognition interviews. They are designed to illustrate patterns in our evaluation process rather than serve as a memorization list. Expect your interviewers to ask follow-up questions that push the boundaries of your initial answers.

Coding & Automation

These questions test your ability to write clean, effective code to solve practical problems you might encounter while supporting or enabling customers.

• Write a function in Python to process a large stream of customer telemetry data and identify error spikes.
• How would you automate the generation of a weekly usage report pulling data from multiple REST APIs?
• Given this legacy JavaScript function that a customer is struggling with, how would you refactor it for better performance?
• Implement a rate-limiter for a mock API endpoint.
• Write a script that orchestrates the deployment of a simple Dockerized application.

Architecture & Debugging

These questions evaluate your systems knowledge and your methodical approach to root-causing complex deployment or networking issues.

• Walk me through the steps you take when a customer's Kubernetes pod keeps crashing in a CrashLoopBackOff state.
• A customer complains that their instance of Windsurf is experiencing high latency. How do you investigate?
• Explain how you would securely connect an on-premise database to a cloud-hosted AI agent.
• What are the common failure points when deploying a containerized application in a highly restricted enterprise network?
• How do you isolate whether a bug is caused by our software, the customer's environment, or an underlying LLM hallucination?

Customer Enablement & Scenarios

These questions assess your communication skills, empathy, and ability to manage customer relationships under pressure.

• Roleplay: I am a customer who is very frustrated because Devin deleted a critical file in my repository. Walk me through your response.
• How would you structure a 60-minute onboarding workshop for a team of senior engineers who have never used AI coding tools?
• Tell me about a time you had to say "no" to a customer's technical request. How did you handle it?
• A customer asks a highly specific question about our product roadmap that you do not know the answer to. What do you do?
• Describe a time you helped a colleague or client reach an "aha" moment with a complex technology.

Behavioral & Startup Fit

These questions look for evidence of autonomy, fast learning, and alignment with our talent-dense, high-execution culture.

• Tell me about a time you had to learn a completely new technology stack in a matter of days to solve a critical problem.
• Describe a process or tool you built from scratch because you noticed an inefficiency in your team.
• How do you prioritize your work when you have three urgent customer escalations happening simultaneously?
• Tell me about a time you disagreed with an engineering decision made by leadership. How did you navigate it?
• Why are you specifically interested in applied AI and software agents?

3. Getting Ready for Your Interviews

Preparing for the Cognition interview process requires a strategic balance between sharpening your core engineering fundamentals and demonstrating profound customer empathy. You should approach your preparation with the mindset of a technical consultant who can dive deep into code.

Expect to be evaluated against the following core criteria:

Technical Proficiency & Systems Knowledge – You must demonstrate strong coding proficiency in Python, JavaScript/TypeScript, or Go. Interviewers will evaluate your ability to write clean, production-ready code, as well as your understanding of distributed computing, containerization, and orchestration technologies like Docker and Kubernetes. You can show strength here by efficiently solving coding challenges and navigating complex architectural discussions.

Debugging & Root Cause Analysis – Enterprise environments are highly complex, and deploying AI agents within them introduces new failure modalities. You will be assessed on your ability to systematically investigate, reproduce, and resolve deep technical issues. Strong candidates will articulate a clear, logical methodology for isolating bugs in unfamiliar codebases or deployment setups.

Customer Enablement & Communication – Since you will act as a teacher and guide, interviewers will test your ability to explain complex technical concepts to diverse audiences. You will be evaluated on your patience, clarity, and ability to drive "aha" moments. You can demonstrate this by structuring your answers logically and maintaining a highly collaborative, consultative tone during pair-programming sessions.

Adaptability & Execution Speed – Cognition operates as a fast-paced, talent-dense startup where autonomy is critical. Interviewers will look for evidence that you can learn exceptionally fast, pivot when necessary, and proactively build processes (like automations or playbooks) rather than waiting for direction. Highlighting your past experiences in early-stage or high-growth environments will signal your readiness for this culture.

4. Interview Process Overview

The interview process for an AI Engineer at Cognition is rigorous, fast-moving, and highly interactive. Unlike traditional software engineering interviews that rely solely on abstract algorithmic puzzles, our process is heavily indexed on real-world scenarios. We want to see how you operate when faced with the actual challenges our customers experience while using Devin and Windsurf.

You can expect a blend of technical assessments and customer-facing roleplay. The initial stages typically focus on validating your core engineering competencies—ensuring you have the foundational coding skills and systems knowledge required to debug enterprise environments. As you progress to the onsite stages, the focus shifts toward applied problem-solving. You will likely participate in pair-programming sessions, mock customer enablement workshops, and deep-dive architecture troubleshooting.

Our interviewing philosophy is deeply collaborative. We evaluate not just whether you arrive at the correct technical answer, but how you communicate your thought process, how you handle pushback, and how you educate your "client" along the way. We are looking for candidates who are energized by teaching and possess the technical depth to back up their guidance.

This visual timeline outlines the typical stages you will navigate, from the initial recruiter screen to the final onsite rounds. Use this to structure your preparation, ensuring you allocate sufficient time to practice both your hands-on coding skills and your live, customer-facing communication. Note that the exact sequence of technical and behavioral rounds may vary slightly depending on whether your background aligns more closely with the Enablement or Support focus areas.

5. Deep Dive into Evaluation Areas

To succeed in the AI Engineer interviews, you must excel across several distinct technical and interpersonal domains. Below is a detailed breakdown of the primary evaluation areas.

Software Engineering & Coding Fundamentals

While this is a highly customer-interactive role, your engineering fundamentals must be rock solid. You will be pair-programming with engineers of varying experience levels, helping them build and automate real projects. Interviewers need to know you can write, review, and debug code effectively in Python, JavaScript/TypeScript, or similar languages. Strong performance here means writing clean, efficient code while clearly narrating your logic.

Be ready to go over:

• Data Structures and Algorithms – Core fundamentals required to optimize scripts, automate workflows, and build internal support tools.
• API Integrations – Writing code to interact with RESTful APIs, handling rate limits, and processing JSON payloads.
• Scripting and Automation – Creating robust scripts to streamline onboarding, deployment, or QA processes.
• Advanced concepts (less common) – Concurrent programming in Go, building custom Model Context Protocol (MCP) integrations, or optimizing memory management in Python.

Example questions or scenarios:

• "Write a Python script to parse a complex log file, identify specific failure patterns, and output a structured JSON report."
• "Walk me through how you would implement an automated testing suite for a new API endpoint."
• "Pair-program with me to build a lightweight integration between a mock internal ticketing system and a third-party service."

Systems Deployment & Architecture

Because Devin and Windsurf operate within sophisticated enterprise environments, you must understand how modern software is deployed and orchestrated. Interviewers will probe your knowledge of distributed computing and containerization. A strong candidate will confidently discuss how different infrastructure components interact and where they typically fail.

Be ready to go over:

• Containerization – Deep understanding of Docker, including image building, networking, and volume management.
• Orchestration – Familiarity with Kubernetes concepts like pods, deployments, services, and ingress controllers.
• Networking Fundamentals – Understanding DNS, proxies, firewalls, and how they impact software deployment in secure enterprise networks.
• Advanced concepts (less common) – Cloud-native security policies, multi-tenant architecture design, or deploying LLM inference endpoints.

Example questions or scenarios:

• "A customer is trying to deploy our agentic tool within their secure VPC, but it cannot reach external APIs. How do you troubleshoot this?"
• "Explain the difference between a Docker container and a virtual machine to a non-technical stakeholder."
• "Walk me through the architecture of a distributed system you recently worked on. What were the bottlenecks?"

Customer Enablement & Problem Solving

This area tests your "consulting intuition" and teacher’s mindset. You will be evaluated on your ability to guide customers through installing, configuring, and optimizing our tools. Strong performance involves active listening, asking clarifying questions, and structuring your guidance so the customer experiences an "aha" moment rather than feeling overwhelmed.

Be ready to go over:

• Technical Translation – Breaking down complex AI or systems concepts into easily digestible explanations.
• Workflow Identification – Analyzing a customer's engineering process to identify high-ROI use cases for Devin.
• Conflict & Escalation Management – Handling frustrated customers experiencing deployment errors or software bugs.
• Advanced concepts (less common) – Designing scalable digital learning curricula or building partner certification models from scratch.

Example questions or scenarios:

• "Roleplay scenario: I am an engineering manager skeptical about adopting AI agents. Convince me of the value using a specific workflow example."
• "A customer reports that Devin is generating sub-optimal code for their specific legacy framework. How do you guide them to improve the agent's context?"
• "Tell me about a time you had to teach a complex technical concept to a diverse audience. How did you measure their understanding?"

6. Key Responsibilities

As an AI Engineer at Cognition, your day-to-day work is a dynamic mix of hands-on engineering, direct customer engagement, and strategic process building. You will serve as the primary technical liaison for enterprise engineering teams, leading interactive programs such as live workshops and pair-programming sessions. During these engagements, you will actively build software alongside customers, teaching them how to leverage Devin and Windsurf to automate tasks, debug legacy codebases, and accelerate their development cycles.

A significant portion of your time will be spent deeply entrenched in customer environments. You will guide clients through complex installations, configure DeepWiki and MCP integrations, and troubleshoot deployment errors. When issues arise—whether from software misuse, underlying bugs, or environmental constraints—you act as the first line of response. You will use your computer science background to root-cause the failure, reproduce the bug locally, and either provide an immediate resolution or escalate the issue seamlessly to our deployed engineering teams.

Beyond individual customer interactions, you are responsible for scaling your impact. You will document failure modalities, extract best practices, and create shared playbooks that turn isolated learnings into repeatable enablement materials. By collaborating closely with Cognition Engagement Managers and our core Engineering teams, you will provide critical product feedback, automate parts of the QA process, and help shape AI Enablement into a structured, measurable product offering that reaches hundreds of thousands of engineers globally.

7. Role Requirements & Qualifications

To thrive as an AI Engineer at Cognition, you must possess a rare combination of strong engineering fundamentals and exceptional interpersonal skills. We look for candidates who are equally comfortable diving into a complex codebase and leading a workshop for a Fortune 500 engineering team.

• Must-have skills – A degree in a STEM field (or equivalent hands-on experience) and 2–3+ years of experience as a software engineer or technical consultant. You must have strong coding proficiency in languages like Python, JavaScript/TypeScript, Java, or Go. Excellent verbal and written communication skills are non-negotiable, as is a proven ability to explain complex technical topics to diverse audiences.
• Systems & Infrastructure Knowledge – You must have a working knowledge of distributed computing, containerization, and orchestration technologies, specifically Docker and Kubernetes. You need to understand how software is deployed in modern enterprise environments.
• Customer Orientation – You must exhibit a strong customer-service orientation, deep empathy, and the ability to thrive in interactive, people-facing environments. You should be energized by coaching others and helping them unlock their potential.
• Nice-to-have skills – Hands-on experience deploying or integrating LLM or agent-based systems in production settings is a major plus. Previous experience founding or working at early-stage startups where autonomy and execution speed were critical will also help you excel. Experience leading technical workshops or developer onboarding initiatives is highly valued.

8. Frequently Asked Questions

Q: How technical is the interview process for the AI Engineer role? The process is highly technical. Even though this is a customer-facing role, you will be interacting with senior enterprise engineers and debugging complex systems. Expect rigorous coding assessments and deep architectural discussions alongside the behavioral and roleplay rounds.

Q: Do I need prior experience building Large Language Models (LLMs)? No, you do not need to be an ML researcher or have experience training foundational models. However, you must have a strong passion for AI, and hands-on experience deploying, integrating, or prompting LLM/agent-based systems in production is a significant advantage.

Q: What is the culture like at Cognition? We are a small, talent-dense team moving at an incredible pace. The culture is highly autonomous, collaborative, and execution-oriented. You will be expected to take ownership of problems, build scalable solutions proactively, and adapt rapidly as our products and customer needs evolve.

Q: How much time should I spend preparing for the pair-programming rounds? Dedicate significant time to practicing live coding while speaking your thoughts aloud. The interviewers care just as much about your communication, collaboration, and receptiveness to feedback as they do about your final compiled code. Practice building small integrations or debugging scripts dynamically.

Q: What is the typical timeline from the first screen to an offer? The process moves quickly, reflecting our startup pace. Typically, candidates complete the entire process—from the initial recruiter call to the final onsite loop—within 2 to 4 weeks, depending on scheduling availability.

9. Other General Tips

• Think Aloud During Technical Rounds: When debugging or writing code, never go silent for long periods. Treat the interviewer like a customer you are pair-programming with. Explain your hypotheses, why you are taking a specific approach, and what you are looking for in the logs.
• Master the Art of the "Aha" Moment: In your behavioral answers, focus on stories where you successfully educated someone. Highlight the specific techniques you used to break down the complexity and how you verified their understanding.

• Structure Your Troubleshooting: When given an open-ended debugging scenario, do not jump straight to a solution. Outline a systematic methodology: gather context, isolate the variables, reproduce the issue, and then apply a fix.
• Showcase Your Bias for Action: We value builders. Emphasize past experiences where you identified a recurring customer issue and proactively built a tool, script, or playbook to solve it at scale, rather than just answering individual tickets.

• Embrace Ambiguity: You will likely be asked questions with missing information or vague constraints. This is intentional. Practice asking sharp, clarifying questions to scope the problem before you begin formulating your answer.

10. Summary & Next Steps

Joining Cognition as an AI Engineer is a unique opportunity to shape the future of software development. You will be on the front lines, introducing the world's most advanced AI software agents to top-tier enterprise teams. By combining your deep technical expertise with a passion for teaching and problem-solving, you will unlock massive value for our customers and help define how AI enablement scales globally.

To succeed in this interview process, focus on demonstrating a flawless balance of technical rigor and customer empathy. Brush up on your core coding skills, review containerization and deployment architectures, and practice articulating your technical decisions clearly and collaboratively. Remember that we are looking for adaptable, fast-learning builders who thrive in high-stakes, ambiguous environments.

This compensation data provides a general baseline for engineering roles in the San Francisco market. Keep in mind that as an early-stage, high-growth startup, Cognition offers highly competitive equity packages that represent a significant portion of the total compensation and upside potential.

Approach your interviews with confidence and a collaborative spirit. Focused preparation on both your technical fundamentals and your communication style will materially improve your performance. For more insights, deep dives into specific technical questions, and interview preparation resources, continue exploring Dataford. You have the skills and the potential to excel—now it is time to show us how you build and teach.