Top AI & Machine Learning Jobs

The role involves building and deploying ML models for core products, solving real-world problems, and collaborating with teams on ML-driven features.

Lead and grow an ML Infrastructure engineering team to design, build, and scale model training, inference, feature stores, and data pipeline systems. Set strategy and roadmap, perform design/code reviews, collaborate with cross-functional stakeholders, hire and mentor engineers, and ensure availability, scalability, operational excellence, and cost management for production ML systems.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

As a Machine Learning Engineer, you will build and deploy models for Snapchat, applying modern ML techniques to solve problems and lead the full ML lifecycle.

Lead vision and roadmap for large-scale recommender systems across Spotlight, Discover, and Friend Stories. Technically lead engineering teams, design next‑generation recommender and retrieval architectures, incorporate SOTA research and foundation models, advance ML infrastructure for scalability and reliability, and partner cross-functionally to drive experimentation, operational excellence, and cost-effective production deployments.

Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Design, build, and optimize large-scale ML infrastructure: embedding generation, batch inference, data storage/compute, data management, quality systems, and production deployments with ML engineers to improve ranking and recommendation systems.

As an intern in the AI Core Architecture team, you'll implement and simulate neural rendering algorithms, benchmark performance, and suggest improvements for NPU efficiency.