Meta

Machine Learning Engineer Interview Experience - California City, California

May 22, 2024

Neutral ExperienceNo Offer

Process

9:15 AM - 9:30 AM CT - Welcome / Office tour

9:30 AM - 10:00 AM CT - Hiring Manager Interview - Round 1

10:00 AM - 11:00 AM CT - ML Theory (Math and stuff) (Comp Vis Appld Sci) - Remote Interview

11:00 AM - 12:00 PM CT - Programming challenge (SQL Data Wizard) - Remote Interview

12:00 PM - 1:15 PM CT - Break

1:15 PM - 2:15 PM CT - ML Optimization (How do you run a model fast) (Intermediate MLE)

2:15 PM - 3:15 PM CT - System Arch and Data Pipelining (Philomathic Data Engineer)

3:15 PM - 3:45 PM CT - No-Tech Interview (VP)

3:45 PM - 4:15 PM CT - Hiring Manager Interview - Round 2 (DRI)

Questions

1 round -> behavior (standard)

Describe projects you've worked on before. Prepare some topics to show your level of complexity and impact/contributions.

Key topics:

Motivation
Growth
Resolution: Trying to solve questions/challenges, resolve disagreements/conflicts, empathy (not just keeping convenience).
Driving results: How to hit the goal, go beyond the goal, prioritize, and how to do the jobs.
How to solve problems independently.
Embrace ambiguity: Be comfortable working without many guidelines or information.
Growth: How to handle feedback from peers and managers, and how to become a better engineer.

Write down the stories using the STAR format. Prepare some cheatsheets.

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Interview Statistics

The following metrics were computed from 1 interview experience for the Meta Machine Learning Engineer role in California City, California.

Success Rate

0%

Pass Rate

Meta's interview process for their Machine Learning Engineer roles in California City, California is extremely selective, failing the vast majority of engineers.

Experience Rating

Positive0%

Neutral100%

Negative0%

Candidates reported having mixed feelings for Meta's Machine Learning Engineer interview process in California City, California.

Meta Work Experiences

Great pay, too much pressure

Machine Learning Engineer • Current Employee

Menlo Park, California, United States

Pros: Pay is competitive. Work on state-of-the-art AI.

Cons: No work-life balance; everyone cares about short-term goals only.

Great pay, too much pressure

Machine Learning Engineer • Current Employee

Menlo Park, California, United States

Pros: Pay is competitive. Work on state-of-the-art AI.

Cons: No work-life balance; everyone cares about short-term goals only.

Good starting point, bad for growth

Machine Learning Engineer • Current Employee

February 24, 2025

Pros: - Could be a fine place at a terminal level. - Free food. - Solid stock payout. - Mostly kind and hard-working ICs.

Cons: - Slow and unclear promotion processes with no level transparency, so you cannot easily assess yourself and your compensation. - Lagging promotions are a way to overwork and underpay for longer, touted as "validating that you are ready for the next step". - Excessive bureaucracy has turned the corner from a startup "build fast" vibe to "you build fast, but we move slow". - Culture of overwork without recognition. It's difficult to set boundaries because of management and aggressive/unrealistic roadmaps. This may not be company-wide, but I have also had experiences with very narrow-minded management that drive hard for the bottom line without being capable of looking at the big picture. - If you work diligently and anticipate/look into risks, but no issues arise, you’re blamed for taking too long, even if you were operating within the planned timeline. - However, if risks had materialized and you hadn’t done due diligence, there would have been complaints about lack of preparation.

Good starting point, bad for growth

Machine Learning Engineer • Current Employee

February 24, 2025

Pros: - Could be a fine place at a terminal level. - Free food. - Solid stock payout. - Mostly kind and hard-working ICs.

Cons: - Slow and unclear promotion processes with no level transparency, so you cannot easily assess yourself and your compensation. - Lagging promotions are a way to overwork and underpay for longer, touted as "validating that you are ready for the next step". - Excessive bureaucracy has turned the corner from a startup "build fast" vibe to "you build fast, but we move slow". - Culture of overwork without recognition. It's difficult to set boundaries because of management and aggressive/unrealistic roadmaps. This may not be company-wide, but I have also had experiences with very narrow-minded management that drive hard for the bottom line without being capable of looking at the big picture. - If you work diligently and anticipate/look into risks, but no issues arise, you’re blamed for taking too long, even if you were operating within the planned timeline. - However, if risks had materialized and you hadn’t done due diligence, there would have been complaints about lack of preparation.

Great Perks, Intense Culture

Machine Learning Engineer • Current Employee

Seattle, Washington, United States

February 3, 2025

Pros: Great perks, food on campus.

Cons: High performance culture every year

Great Perks, Intense Culture

Machine Learning Engineer • Current Employee

Seattle, Washington, United States

February 3, 2025

Pros: Great perks, food on campus.

Cons: High performance culture every year

Great pay, too much pressure

Machine Learning Engineer • Current Employee

Menlo Park, California, United States

Pros: Pay is competitive. Work on state-of-the-art AI.

Cons: No work-life balance; everyone cares about short-term goals only.

3.0
Great pay, too much pressure
Machine Learning Engineer • Current Employee
Menlo Park, California, United States
May 12, 2025
Pros: Pay is competitive. Work on state-of-the-art AI.
Cons: No work-life balance; everyone cares about short-term goals only.

Good starting point, bad for growth

Machine Learning Engineer • Current Employee

February 24, 2025

Pros: - Could be a fine place at a terminal level. - Free food. - Solid stock payout. - Mostly kind and hard-working ICs.

Cons: - Slow and unclear promotion processes with no level transparency, so you cannot easily assess yourself and your compensation. - Lagging promotions are a way to overwork and underpay for longer, touted as "validating that you are ready for the next step". - Excessive bureaucracy has turned the corner from a startup "build fast" vibe to "you build fast, but we move slow". - Culture of overwork without recognition. It's difficult to set boundaries because of management and aggressive/unrealistic roadmaps. This may not be company-wide, but I have also had experiences with very narrow-minded management that drive hard for the bottom line without being capable of looking at the big picture. - If you work diligently and anticipate/look into risks, but no issues arise, you’re blamed for taking too long, even if you were operating within the planned timeline. - However, if risks had materialized and you hadn’t done due diligence, there would have been complaints about lack of preparation.

2.0
Good starting point, bad for growth
Machine Learning Engineer • Current Employee
February 24, 2025
Pros: - Could be a fine place at a terminal level. - Free food. - Solid stock payout. - Mostly kind and hard-working ICs.
Cons: - Slow and unclear promotion processes with no level transparency, so you cannot easily assess yourself and your compensation. - Lagging promotions are a way to overwork and underpay for longer, touted as "validating that you are ready for the next step". - Excessive bureaucracy has turned the corner from a startup "build fast" vibe to "you build fast, but we move slow". - Culture of overwork without recognition. It's difficult to set boundaries because of management and aggressive/unrealistic roadmaps. This may not be company-wide, but I have also had experiences with very narrow-minded management that drive hard for the bottom line without being capable of looking at the big picture. - If you work diligently and anticipate/look into risks, but no issues arise, you’re blamed for taking too long, even if you were operating within the planned timeline. - However, if risks had materialized and you hadn’t done due diligence, there would have been complaints about lack of preparation.

Great Perks, Intense Culture

Machine Learning Engineer • Current Employee

Seattle, Washington, United States

February 3, 2025

Pros: Great perks, food on campus.

Cons: High performance culture every year

5.0
Great Perks, Intense Culture
Machine Learning Engineer • Current Employee
Seattle, Washington, United States
February 3, 2025
Pros: Great perks, food on campus.
Cons: High performance culture every year

Meta Interview Questions

Minimum Remove to Make Valid Parentheses

Given a string with parentheses, determine the minimum number of parentheses you must remove to make the string valid. A valid string has balanced and properly nested parentheses.

StringsStacksTwo Pointers

Valid Word Abbreviation

Determine if a given abbreviation is valid for a given word. An abbreviation can contain digits representing the number of skipped characters.

StringsTwo Pointers

Binary Tree Vertical Order Traversal

Traverse a binary tree and return a list of node values for each vertical line, ordered from left to right. Nodes on the same vertical line should be ordered from top to bottom.

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