Machine Learning @ Snap
Machine Learning Engineers at Snap work end-to-end and own the entire respective Machine Learning system.
Machine Learning Engineering at Snap
As a Machine Learning Engineer at Snap you’ll drive Snapchat’s dynamic experience through the full lifecycle of advanced state-of-the-art models – from data preprocessing, feature engineering and model training, to deployment and ongoing optimisations. You’ll leverage cutting-edge techniques, ranking algorithms for ad relevance, recommendation engines for personalised content and NLP for enhanced interactions – all while processing petabytes of data for over 850 million users.
Through both classic and deep learning models, you’ll create precise, responsive experiences that empower users to express themselves, connect and discover the world in real-time.
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Our machine learning engineers solve real world ML problems.
Monetisation
As a Machine Learning Engineer on the Monetisation team you’ll build and optimise the entire ad ecosystem. You’ll drive high-relevance and high-impact for not only advertisers and users but for all of Snap. From designing high-performance systems for real-time bidding or ad serving and auctions, personalising light and heavy rankers, creating solutions for ad targeting and delivery, you’ll continue to ensure seamless integration of ads across the platform. You’ll train models on billions of examples, using multi-task learning, sequence modelling and user x ad interaction modelling. Our models predict user demographics to improve audience targeting with graph neural networks and content, to understand how our work shapes the future of Snapchat’s ad platform.
What you’ll work on:
AI-driven advertising
New personalised ad products and experiences
Owner of Snap’s main revenue driver
Developing cutting edge ad products
Interview Process
Below is an overview of the interview rounds and competencies that may be assessed throughout the ML interview process. Depending on your target level, the order of these rounds may differ or you might have some rounds omitted. Each technical interview will begin with approximately 15 minutes of behavioral discussion focused on experiences aligned with Snap’s values. Following your answer, your interviewer will transition into the technical portion of the interview assessing one of the below competencies:
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Our interview process covers engineering, foundational, and applied ML.
Coding
Expect to solve algorithmic problems that test your proficiency in data structures, algorithms, and problem-solving skills. Focus on your ability to write clean, efficient, and well-documented code.
ML Fundamentals
You’ll be assessed on ML theory and core machine learning models, concepts, techniques and applications. Be prepared to discuss supervised and unsupervised learning, recommendation systems, ranking, model evaluation metrics, and optimisation techniques.
ML Applied Design
Evaluates your ability to design and apply machine learning solutions to real-world problems. You may be asked to walk through the end-to-end process of selecting models, feature engineering, and evaluating performance. At times this can test your ability to problem-solve in an ambiguous environment.
ML System Design
The focus will be on designing scalable and robust ML systems that can handle large-scale data and production environments. Expect to discuss the infrastructure and trade-offs in architecture, model deployment strategies and system monitoring.
General Coding
This will be focused on general computer science fundamentals. You can expect algorithms and data structure question(s) for this round and you are able to code in your language of preference.
Leadership
Depending on the target level, candidates may participate in a leadership interview focused on collaboration, influence, ownership, communication, mentorship, and decision-making. Interviewers will assess how you navigate ambiguity, drive impact across teams, and support effective engineering practices, including thoughtful adoption of AI-assisted workflows, through examples from your past experience.
Product
This round is required for L6+ interviews and will be conducted by a Product Manager or an ML Engineer. The objective is to assess your ability to understand customer needs, build business domain knowledge, bridge technical ML solutions and product requirements, and partner cross-functionally with Product Managers.
Q&A
The Q&A session will not include any formal technical or behavioral competency assessment like other rounds. Instead, it is an opportunity for candidates to learn more about the team, projects, and company. Candidates are encouraged to come prepared with thoughtful questions about the role, team culture, technical challenges, cross-functional collaboration, growth opportunities, or life at Snap.
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