Dice is the leading career destination for tech experts at every stage of their careers. Our client, Jobot, is seeking a Senior ML Engineer / Lead Machine Learning Engineer for their growing subsidiary of a large public company. This 100% remote position offers a competitive salary of $155,000 to $235,000 per year based on experience, along with various other perks ranging from stock grants to parental leave and flexible work accommodations.

As a Machine Learning Engineer with Dice, you'll be responsible for designing, building, and deploying advanced machine learning models, as well as collaborating with teams to drive innovation and efficiency. This guide will walk you through the interview process, typical questions, and invaluable tips to help you land your next role. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Dice as a Machine Learning Engineer. Whether you were contacted by a recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from Dice’s Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the recruiting manager or hiring manager may be present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Dice’s data systems, ETL pipelines, and Python coding challenges.

In the case of more senior roles, expect questions on designing, training, and evaluating machine learning and AI models, specifically within the AWS ecosystem. Your proficiency with MLOps practices, batch and streaming pipelines, and orchestration tools like Airflow will also be assessed.

Depending on the seniority of the position, take-home assignments regarding model performance monitoring, automated model deployment, and data drift detection might be incorporated.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. During your time at Dice, you might go through multiple interview rounds, varying with the role. Your technical prowess, including deep learning, software development, and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Machine Learning Engineer role at Dice.

Quick Tips for Dice Machine Learning Engineer Interviews

• Know Your Tools: Dice places a high value on proficiency with frameworks and libraries for machine learning & AI such as TensorFlow, PyTorch, and scikit-learn. Ensure you’re up-to-date with these tools.
• Understand MLOps: Given the importance of MLOps practices at Dice, brush up on automated model deployment, performance monitoring, and data engineering concepts.
• Showcase Cloud Expertise: Dice emphasizes experience with cloud platforms like AWS. Highlight your experience architecting solutions on such platforms and your familiarity with associated tools like Docker, Kubernetes, and CI/CD pipelines.

Typically, interviews at Dice vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• How would you measure the effectiveness of extra pay for delivery drivers during peak hours? You work at a food delivery company. How would you measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet consumer demand?

• Create a function rain_days to calculate the probability of rain on the nth day after today. The probability that it will rain tomorrow depends on whether it rained today and yesterday. If it rained both days, there's a 20% chance it will rain tomorrow. If it rained one of the days, there's a 60% chance. If it rained neither day, there's a 20% chance. Given it rained today and yesterday, write a function to calculate the probability it will rain on the nth day after today.

• What are the assumptions of linear regression? List and explain the key assumptions that must be met for linear regression analysis to be valid.

Dice is your gateway to a promising career in tech, offering a range of positions tailored for Machine Learning Engineers, from anywhere in the remote work spectrum to hybrid roles. Whether you're aspiring to be a Senior Machine Learning Engineer at Jobot, an MLOps Engineer with IT Associates, or join Vanguard’s dynamic team, Dice has you covered. If you're looking for insights and guidance on acing your interview and landing these coveted roles, dive into our comprehensive Dice Interview Guide on Interview Query. Empower your interview prep with tailored resources and expert advice for roles like Machine Learning Engineer, MLOps Engineer, and more. Ready to unlock your potential? Check out our company interview guides for even more preparation tips. Let's conquer that interview together! Good luck!