
Machine Learning @ Snap
Machine Learning Engineers at Snap work end-to-end and own the entire respective Machine Learning system.

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.
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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Platform
As a Machine Learning Engineer on the Platform Engineering team you’ll build models that provide timely, accurate, high-quality and actionable data for decision making that forms the backbone of Snap's external reporting, experimentation, and ML/Ranking. Snap’s AI platform (aka Bento) powers many large-scale ranking, recommendation, and Gen AI use cases. Over the past year, our scale of operations increased by 4-10X. You’ll develop embeddings that are consumed by downstream modelling teams. This team is mission critical to scaling and diversifying Snaps ML model offerings.
What you’ll work on:
Highly available, multi-cloud infrastructure
Optimising the entire developer experience
Ship high-quality features fast

Trust & Safety
We want Snapchat to be a safe and positive experience for everyone who uses our platform or products. For this reason, we created rules and policies that explain the rights and responsibilities of all members of our community.
What you’ll work on:
Build Privacy-preserving AI
Launch new safety features

Content
As a Machine Learning Engineer on the Content team you’ll work on developing features for highly visible short form video recommendation models that power the creativity and connection at the core of our platform like Stories and Spotlight. You’ll focus on enhancing user experience, driving innovation across Spotlight, Lens ranking, supporting content creators and making content creation effortless. Our team tackles multi-modal content understanding, using advanced graph representation learning and content ranking to surface the best experiences for our users. We optimise machine learning models through human review and leverage state-of-the-art personalisation techniques, placing us at the forefront of short form video content innovation.
What you’ll work on:
Personalised content recommendations
Generative AI for content creation
Develop features for highly visible products like Spotlight, Stories, Discover Feed & Friend suggestions

Core Growth
As a Machine Learning Engineer on the Core Growth team you’ll drive the search and friending experience across 15+ products, supporting over 100 million daily active users. Our team powers search for friends and content using advanced technologies, including graph databases, Large Language Models (LLMs), Mixture of Experts (MoE), and sophisticated machine learning stacks. You’ll develop search algorithms that connect users through friend and communication graphs, use visual and text embeddings for content discovery and enhance search with pretype recommendations that predict users queries. In addition to optimising search infrastructure, the team’s friending products help users make real, meaningful connections to responsibly grow the Snapchat community. This work strengthens user engagement and retention, enabling other Snapchat features like messaging and story maps to flourish at scale.
What you’ll work on:
Create AR experiences
Large-scale system (5B notifications daily)
Snapchat’s graph with 1+billion of nodes and hundreds of billions of edges

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

generative AI
Our generative AI team is at the forefront of innovation, shaping the future of augmented reality (AR) through SnapAR, which includes Lens Studio, Lens Engine, and Lens Cloud. Our engineers build real-time advanced AI models and machine learning models for dynamic transformations, such as realistic filters, object recognition, and scene understanding, ensuring seamless AR interactions for products like ML Lenses and Dreams. Lens Studio 5.0 stands out as the leading developer platform for creating cutting-edge Lenses that blend generative AI with traditional computer vision techniques. generative AI empowers creators to design engaging lenses effortlessly without requiring expertise in 3D modelling, animation or coding.
What you’ll work on:
Develop and optimise generative AI Models
Build AI-driven tools like text-to-3D generation
Deliver generative AI and computer vision powered features