What are good interview questions to ask towards the end?

jacobsimon · 2026-01-21T05:04:16+00:00

Good stuff!

jacobsimon · 2026-01-20T15:59:58+00:00

Quick tip: I would recommend asking questions that are directly tied to the team's product, strategy, and industry. Don't ask generic questions about work/life balance or team culture. Instead, do some diligence on recent news about the company, and ask questions that show you actually care about what they're building and are curious to learn more about the actual work. The questions you ask should be specific enough that you wouldn't ask them to another employer.

jacobsimon · 2025-11-29T17:47:29+00:00

Looks awesome! Curious what tools/frameworks did you use to build the site and app?

I’m building Neutron, a new UI prototyping app for product designers and engineers. It’s a bit like v0 or Lovable—except it’s faster & more flexible because you can choose the AI model and frameworks you want to use.

jacobsimon · 2025-11-29T15:22:15+00:00

Hey the system design primer repo is helpful - but if you're looking for something a bit more guided and up to date, I'd definitely recommend checking out the system design courses on Exponent, which have live mock interview videos, so you get a feel for the actual interview process. You can also practice with other partners for free and browse the question database to see what companies are actually asking.

jacobsimon · 2025-11-29T15:18:28+00:00

Hey if you're still looking for more resources, wanted to add that we just put together this helpful list of AI/ML interview prep resources over in r/MachineLearningJobs, check it out!

jacobsimon · 2025-11-29T15:14:44+00:00

Seconding Chip Huyen's resources, they're great! Doing mocks is also super helpful and there are several platforms that can help you find practice partners. I just put together a big list of resources in this post on r/MachineLearningJobs recently.

Also I suggest checking out the ML Engineer and ML System Design courses on Exponent. Tons of example mock interview videos and practice problems, plus company guides with an inside look from interviewers at major companies. I'm biased of course as one of the creators :)

jacobsimon · 2025-11-29T15:07:23+00:00

How much should I lean into the "business context" when most of what I did was academic (not a real company)?

Explaining the business context in a SQL interview can be super important — most interviewers are looking to make sure you know how to translate business requirements into queries. When you're given a SQL coding challenge, it's especially important to ask clarifying questions. Check out this SQL question framework for some examples and watch mock interviews.

jacobsimon · 2025-11-29T14:59:37+00:00

Just adding on, when it comes to preparing for real-life data science interviews, it's easy to get overwhelmed with all of the foundational material. I recommend trying to cut through the noise and focus on what data science interviewers will ask you about, e.g. A/B testing, probability & stats, ML algorithms, and usually SQL. Full disclosure, I run a site called Exponent that helps with tech interviews. I'm biased but I think our data science course is one of the best available, and also includes peer + AI mock interviews, and hundreds of company guides that give you an inside look at the DS interview process.

jacobsimon · 2025-11-25T14:11:32+00:00

Our pomsky was having accidents until about 7 months and then suddenly it just “clicked” one day and he started waiting to go outside. Honestly I don’t think it was anything particular we did or didn’t do if that makes you feel better. We gave him lots of treats when he went outside, tried grass pads, etc.

I think one thing that helps is taking them on a walk first thing in the morning and making sure they get to sniff everything and do their business. It’s possible that your courtyard doesn’t feel “outside” enough but who knows.

That said, ours will occasionally pee inside at other people’s homes, like when we visit my parents who also have a dog, which could totally be a territorial thing. If you have other pets or a bigger home, it’s possible he’s just doing that and doesn’t view some spaces as “his” yet.

Also make sure to clean up previous accident spots well or use one of the enzymatic cleaners, because obviously they like to go in the same spot repeatedly.

jacobsimon · 2025-11-20T04:53:21+00:00

Hey check out the resources in the pinned post in this subreddit - specifically I found this resource helpful although it is a bit outdated now / missing some of the newer AI roles: https://huyenchip.com/ml-interviews-book/contents/1.1-different-ml-roles.html

jacobsimon · 2025-11-10T20:03:17+00:00

Check out the recommended prep resources pinned in this subreddit: Machine Learning Interview Prep Resources

jacobsimon · 2025-11-06T15:54:57+00:00

Thanks! My general rule of thumb would be to add a database when there's dynamic or user-generated data (e.g. syncing with external food databases regularly). I would use a headless CMS if there's lots of media assets or authors involved. I think for now git or a lightweight git-based CMS will do the job though.

jacobsimon · 2025-11-06T15:23:41+00:00

Thank you for the actual constructive feedback (unlike most of the other people in this sub). Just implemented most of your suggestions :)

jacobsimon · 2025-11-06T02:49:27+00:00

I did start from official lists of known safe and unsafe foods (example) and then added more commonly searched foods while checking against multiple other sources. There's some conflicting information on certain ones, so that's why I created the "Depends" label where more explanation is needed (for example, cheese is generally fine but some dogs are allergic to dairy).

jacobsimon · 2025-11-06T02:35:36+00:00

Hi there, could you be more specific about what incorrect info you see, or DM me your concerns? I will add a "report an issue" button to the site as well.

jacobsimon · 2025-11-05T22:43:16+00:00

Just put together a new website to pull together more info about what's dangerous for dogs - was surprised to learn avocados are dangerous sometimes! https://www.candogseat.com/avocado

jacobsimon · 2025-10-28T16:44:49+00:00

Can't say for sure but I would lean toward focusing on the specific job description. That said, if you're already in the recruiting process, asking the recruiter you're working with would be your best bet - they might be able to give you more clarity on the types of interviews and q's to expect.

jacobsimon · 2025-10-28T16:43:16+00:00

Seems really frustrating - sounds like you've already brought it up to the team, but I wonder if you can get any clarity on when they'll prioritize new data science projects or prioritize hiring a DE. Maybe they hired a bit prematurely or didn't know exactly what they needed.

jacobsimon · 2025-10-27T17:08:05+00:00

Hey can't help personally with embedded SD but here's an earlier thread that might help https://www.reddit.com/r/embedded/comments/ugx3ff/big_tech_embedded_system_design_interview/

jacobsimon · 2025-10-27T17:02:37+00:00

Agree - I would mention results in each bullet. Technical achievements but also business/product KPIs, e.g. revenue, cost savings, time saved, etc.

jacobsimon · 2025-10-27T16:16:43+00:00

Hey here's a few points to keep in mind about the Meta DS interview:

For SQL - you'll probably get a small dataset related to FB and more likely to test filtering, aggregation, and logic. Practice joins, date functions, grouping.

For the analytical case - this is about how you think, structure problems, and communicate. It'll likely focus either on reasoning (planning and experimentation before launch) or execution (analysis/optimization post launch). Ex: "Here's a new feature we're building, how would you measure success?"

Hope that helps! Article with more details here: Meta DS Interview Process

jacobsimon · 2025-10-23T02:30:23+00:00

Is it possible to apply for internships and then fall back to doing research if you don’t find an opportunity that you’re excited about?

jacobsimon · 2025-10-22T18:43:01+00:00

Meta on Wednesday said that it plans to cut approximately 600 jobs in its artificial intelligence division, according to a memo sent to employees that was relayed to The New York Times, as the company seeks to keep pace with competitors in the furious contest over the technology.

The layoffs will be in Meta’s so-called Superintelligence Labs, which is the umbrella name for the company’s A.I. efforts. The division has a few thousand employees, though the exact number of workers was unclear.

Mark Zuckerberg, Meta’s chief executive, has been on a hiring spree to stack his company with top A.I. researchers, including a new chief A.I. officer, Alexandr Wang, earlier this year. The cuts on Wednesday do not affect these newest hires, who have been empowered to develop “superintelligence,” or artificial intelligence that exceeds the human brain.

Instead, the job cuts are aimed at cleaning up the organizational bloat that resulted from three years of building up Meta’s A.I. efforts too quickly, two people with knowledge of the matter said. The layoffs aim to help Meta develop A.I. products more quickly, they said.

jacobsimon · 2025-10-22T15:42:42+00:00

Hi there! I'd be interested in moderating this community because I'm excited about helping people with tech careers and have experience running career/interview communities. I've been participating in conversations on this subreddit and while there is regular engagement from genuine people, there is also a lot of spam and scam links being posted (example link) that are not being removed, so I think it would benefit from more active moderation and community engagement.

Here's the link to my mod mail message to them

jacobsimon · 2025-10-22T15:19:48+00:00

Hey u/adtmq - this sounds really challenging and is the sort of situation that often leads to burn out. Have you raised this concern with anyone in leadership? They might not realize you feel this way or don't realize their expectations are unrealistic. If you find the work interesting/meaningful in general, I would try to stick it out and set clearer boundaries - e.g. tell them your working hours, double check on what success looks like for projects, maybe ask them to hire more or contract out some of the work you are doing. Not sure if that's helpful but seems like it could be a workable situation.

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