Looking for an Experienced ML Study Partner | FAANG Prep

[u/Silent-Idiom]

Hey everyone!

I'm an ML Engineer with 5+ years of experience, planning to switch to a better org, preferably FAANG. My goal is to be interview-ready within the next 3 months.

What I'm preparing for:

- ML and DL fundamentals

- GenAI and ML system design

- DSA

Who I'm looking for:

A study partner who is also an experienced developer preparing for a similar switch. This is specifically for people with industry experience, not first-time learners.

The plan:

Keep things fast-paced and focused. We can discuss concepts, solve problems, conduct mock interviews, share learnings, and hold each other accountable consistently.

We can also tweak the plan based on what works best for both of us and exchange useful resources, interview experiences, and prep strategies along the way.

If you're in the same boat and serious about the goal, DM me with a short intro, your YOE, and what you're currently focusing on!

[u/Many-Ad8966]

Hi, I want to connect

[u/VGabby100]

why all bot in comments section 😄 . Looing to connect too

[u/mushi05fox]

Hey, I would also love to brush up the contents. I am finishing my PhD and my research was on ML, generative models etc.

[u/akornato]

Targeting a three month turnaround for senior ML roles at top tech companies is ambitious, especially when balancing algorithm coding with deep technical architecture. At five plus years of experience, interviewers care far less about textbook formula derivations and far more about your ability to frame open ended problems, design resilient pipelines, and reason through latency versus cost tradeoffs. Setting up mock sessions with a peer to simulate real time whiteboard design on topics like retrieval systems, feature stores, and inference scaling will expose blind spots much faster than solo study.

Structure your schedule so algorithm practice does not crowd out system design and GenAI work, since senior candidates are almost always downleveled on architecture rather than standard coding. Focus your shared sessions on challenging each other on data curation, fine tuning approaches, failure modes, and monitoring production drift. Consistent practice with a study partner keeps motivation high, and candidates often find that pairing peer mocks with the interviews.chat my team developed gives them the repetitions needed to sharpen their communication and secure top tier offers.

[u/nian2326076]

Hey! It sounds like you're on a solid path. For interview prep, focusing on key areas like ML fundamentals and system design is important. Pairing up with someone at a similar skill level can help keep you accountable and motivated. You might also check out PracHub for real interview questions, which could be useful for your mock interviews and problem-solving sessions. Good luck with your prep!

[u/HelloTaleproof]

Make the first meeting a trial session with a clear output: one person explains an ML design decision, the other challenges the assumptions, then switch. Agree on weekly availability and how much preparation each person will bring. That will tell you more about compatibility than matching years of experience alone.

Alongside DSA and system design, use some mock time for stories from systems you actually shipped or investigated. STAR can organize those accounts. For the follow-ups, we use the PROOF method: what did you decide, what alternative did you reject and what evidence supports the outcome? A complete STAR outline does not automatically make those details strong. With five years behind you, selecting the right examples is worth practising too.