Fast-moving with growth opportunities
Fast-moving with growth opportunities
Fast-growing tech company with a high-quality ML team in Sydney, responsible for driving customer relevancy across billions of e-commerce transactions each year. This presents plenty of opportunities to work on challenging data science / ML problems at scale. Salaries and benefits are generous, including all team members participating in the company stock plan.
More senior roles need to invest regular time collaborating with colleagues in NY, resulting in early morning meetings and regular travel.
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Rokt Interview Questions
Rokt Interview Experiences
Machine Learning Engineer
A simple online interview with the team lead about my experience and a few technical questions. I applied for a junior role, but they changed it to a graduate role.
Machine Learning Engineer
The interviewer at Rokt was very friendly, making the process relaxed and comfortable. The interview lasted about an hour and focused on technical questions. Overall, the atmosphere was welcoming and pleasant.
Machine Learning Engineer
The interview process included a technical round focused on a machine learning problem. The specific task was to design and discuss a recommendation system. Be prepared to talk about different approaches and how you would evaluate the model.
Machine Learning Engineer
Sydney, New South Wales, AustraliaJanuary 1, 2025A simple online interview with the team lead about my experience and a few technical questions. I applied for a junior role, but they changed it to a graduate role.
Machine Learning Engineer
Sydney, AustraliaJuly 4, 2025The interviewer at Rokt was very friendly, making the process relaxed and comfortable. The interview lasted about an hour and focused on technical questions. Overall, the atmosphere was welcoming and pleasant.
Machine Learning Engineer
SydneyJuly 1, 2025The interview process included a technical round focused on a machine learning problem. The specific task was to design and discuss a recommendation system. Be prepared to talk about different approaches and how you would evaluate the model.
