My Journey Preparing for the Anthropic MLE Interview

I’ve always known that Anthropic’s Machine Learning Engineer (MLE) interviews are not only highly demanding but also place a significant emphasis on culture fit. After going through the rigorous interview process, from recruiting on LinkedIn to facing a rather daunting virtual on-site (VO) interview, I wanted to share my experiences and insights.

Timeline of My Interview Process

• June 10: Initial Screening
• July 5: Technical Phone Interview
• July 20: Virtual On-site Interview

Technical Interview Breakdown

The technical phone interview spanned 45 minutes and involved both coding and machine learning theory:

• Coding Challenge: Implement a custom attention mechanism for a small-scale Language Model similar to Claude’s architecture.
• ML Theory Question: Explain how Reinforcement Learning from Human Feedback (RLHF) works in the context of Anthropic’s safety-first approach.

Following up, I was asked how to prevent overfitting in reward models.

Virtual On-site Interview Structure

During the VO, I faced several components, including:

Coding Challenges

• Round 1: Optimize the inference speed of a Claude-like model for mobile devices.
• Round 2: Write a function aimed at detecting and mitigating bias in the output of a Language Model, aligned with Anthropic’s guidelines.

System Design Task

We were asked to design a distributed training system for a large-scale Language Model, akin to the one utilized for Claude.

Technical Project Discussion

This involved discussing my previous LLM projects to illustrate my experience and skills.

Culture Fit & Leadership Discussion

The interviewers focused on my alignment with Anthropic’s values, especially around safety-first priorities and collaboration for AI alignment. One key question was about past experiences where I had to make safety-related decisions on a project.

Feedback and Next Steps

On August 5, I received positive feedback from HR. However, they indicated that I needed to strengthen my leadership skills and suggested a follow-up conversation with a senior manager. The subsequent discussion confirmed that they appreciated my understanding of driving innovation in AI safety. Now, I await a team match, noting that Anthropic has diverse resources and directions across teams.

Key Takeaways from My Anthropic Interview Experience

1. Safety and Explainability: Emphasize safety-first perspectives and relevant optimizations in coding and system design questions.
2. Research Company Blog and Papers: Many real interview questions inspire from their blog posts and academic papers, such as technical details and training optimizations for the Claude model.
3. Prepare Failure Cases: Be ready to discuss instances where model performance didn’t meet expectations, showcasing your growth mindset in how you seek to learn and improve.

To wrap up, I’ve compiled my notes on the Anthropic MLE interview, including coding challenges, system design ideas, and theories, which you can access here: 👉【A】. I wish everyone the best of luck in their job hunts this fall! 🍀 #TransitionToTech #InternationalStudents #InterviewExperience #JobSearchInUSA #MLE #SoftwareEngineering #TechCareer