Machine Learning Engineer Interview Experience - New York, New York
Process
I applied online. The process took 1+ week.
Initial call with CTO followed by a take-home task for 4 to 6 hours. The task was to design an end-to-end deep learning model given a dataset. At the end, there was a discussion with the team about the code submitted. Got a rejection email with a standard template after 2 days. Quite disappointing to see a template answer even though I invested 8 hours for the interview process.
Questions
What is PCA
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Interview Statistics
The following metrics were computed from 1 interview experience for the Synthesia Machine Learning Engineer role in New York, New York.
Success Rate
Synthesia's interview process for their Machine Learning Engineer roles in New York, New York is extremely selective, failing the vast majority of engineers.
Experience Rating
Candidates reported having very negative feelings for Synthesia's Machine Learning Engineer interview process in New York, New York.
Synthesia Work Experiences
- 5.0One of the best places to workSenior Software Engineer • Current EmployeeMarch 13, 2023Pros: - Amazing product - Very good company culture - Interesting technical challenges - Very good salary packageCons: I haven't found one yet.
- 4.0Good company to work forSoftware Engineer • Current EmployeeLjubljanaApril 3, 2026Pros: - Freedom to work whenever - Good salary - Great coworkersCons: - Lack of mentorship - Pressure to perform
- 5.0🚀 Great Product, Amazing Autonomy!Senior Software Engineer • Current EmployeeLondon, EnglandJanuary 13, 2025Pros: - High trust and low process culture that encourages ownership, goal-setting, and impactful work. - A supportive, people-first environment that values learning and responsible AI innovation. - Competitive pay, flexible work, 25 days of PTO, and strong career growth opportunities in a low-hierarchy setting.Cons: As a fast-growing company, some processes are still evolving, which can occasionally lead to ambiguity. However, this also creates opportunities for employees to contribute to building and improving workflows.
