Real Interview Experiences
Learn what to expect directly from candidates and interviewers who've been through it.
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“It was pretty easy, I broke down the funnel into small steps and then try to optimise for each step one by one by asking questions. But I haven't asked for the formula to define the process or metric they are trying to optimise which I realised mid interview but the interviewer was pretty sweet ”
“By far the most technically challenging interview process I've ever had to do for new grad product management roles. Successive coding questions in a constrained time block.”
“It was an AI humanoid-led interview with no human in the loop, which was a first for me. The questions were well-structured and gave me enough space to respond fully. That said, interacting with a humanoid AI felt surreal at times — it was hard to shake the feeling of speaking to something that looked human but wasn’t. It took a few minutes to settle in and focus on the content of my answers rather than the format.”
“Very smooth and clear, the recruiter laid down what the expectations are, and the coding interview was mostly leetcode medium level.”
“Overall, I've really enjoyed the interview process so far. What stood out to me was that each conversation felt a little different and gave me a better understanding of what Deployed Engineering actually looks like at LangChain. The early conversations focused more on my background, the types of customers I work with today, and some of the AI projects I've been involved in. As the process moved forward, the discussions became more technical, but not in a "gotcha" kind of way. The team seemed much more interested in how I think through problems, how I work with customers, and how I approach building and operating AI systems than whether I could recite framework internals from memory. The peer interview was probably my favorite part so far. It felt more like a conversation between engineers than a traditional interview. We spent a lot of time talking about agents, coding assistants, observability, evaluation, customer adoption, and where the industry is heading. I left that conversation feeling like I had learned something, which isn't always the case in interviews. I think one thing that went well was being able to connect my experience at Databricks to the kinds of challenges LangChain customers are facing. A lot of the conversations around AI over the last year have shifted from "Can we build this?" to "How do we trust it?", "How do we debug it?", and "How do we get it into production?" Those are discussions I've been having with customers already, so I felt comfortable speaking about them. If there's an area that challenged me, it was probably realizing just how broad the LangChain ecosystem has become. Going into the process, I was familiar with LangChain and had spent time around LangGraph and LangSmith, but the interviews made it clear that the role requires understanding not just how to build agents, but how to help customers evaluate, improve, and operate them over time. There are definitely areas where I'm still learning, particularly around some of the deeper platform and infrastructure topics, but I think I was honest about where my strengths are and where I'm continuing to grow. More than anything, the process has reinforced why I'm interested in the role. The combination of technical depth, customer interaction, problem solving, and being close to where agent technology is evolving feels very aligned with what I enjoy doing and where I want to continue developing my career. ”
“The overall interview process was incredibly smooth, straightforward, and well-organized from start to finish. The recruiter was exceptionally friendly, responsive, and did a fantastic job of setting clear expectations for each stage. There weren't any significant downsides, as the scheduling was efficient and the communication remained consistent throughout.”
“The process was smooth in terms of communication and scheduling. The people I met were incredibly kind and high caliber. They were highly personable and seem they are trying to find others who can continue its culture of excellence.”
“The recruiter was warm and friendly. The conversation went well. Moving on to second round, where we'll dive more deeply into 2 specific examples.”
“The system design interview was data collection, SFT and RL stages. It went well for me. It was focused on LLM post training techniques. The ML coding was worst for me although I practiced the same question but using PyTorch and doing without PyTorch and without any documentation was challenging for me.”
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“The weirdest Anthropic round was the company values interview. It was almost like a therapy session, and honestly if you went to a therapist at some point, you will pass that round much more easily.”
“What was very unusual is they didn’t give me any tooling to draw the system design, so I just sketched it on a piece of paper and talked them through it, then we got into this oddly deep debate about whether hover-over history should count as a recommendation signal.”
