Top AI & Machine Learning Jobs

Build and operate large-scale ML infrastructure, including feature pipelines, model training and evaluation systems, high-performance inference services, data management platforms, and model orchestration tools. The role partners with ML engineers to deploy models into production, improve system reliability and efficiency, and maintain secure, scalable, production-ready services using Python, Java, and related AI technologies.

Build and operate large-scale machine learning inference infrastructure for recommendation models, embedding-generation models, and self-hosted LLMs. Responsibilities include model optimization, export, serving, hosting, LLM fine-tuning, reinforcement learning infrastructure, and inference performance improvements. The role supports highly available ML systems across Snap’s advertising, content, growth, camera, and other product teams.

Conduct research in generative media, large generative models, GenAI efficiency, personalization, 3D/4D generation, visual-language models, large-scale data, and inference frameworks. Collaborate with researchers and engineers to develop state-of-the-art technology, with opportunities to publish papers and file patents. The 13–16 week internship requires working in an office at least four days per week.

Lead research in language models, personalization, recommendation systems, information retrieval, and generative modeling. Build scalable machine learning prototypes, conduct large-scale distributed experiments, publish findings, mentor junior researchers and interns, and partner with engineering teams to deploy technologies serving Snapchat users. The role requires strong Python and PyTorch expertise, research leadership, publications, and experience applying language models to search, ranking, or personalization.

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Build and lead production machine learning capabilities for Snapchat+ subscription growth and monetization. Responsibilities include developing personalization, ranking, propensity modeling, offer decisioning, retention, and lifecycle optimization systems; translating ambiguous business problems into ML solutions; conducting experimentation; deploying scalable services; and establishing technical direction. The role partners with Product, Data Science, Backend, and Mobile Engineering teams while measuring impact on revenue, subscriber value, and retention.

Leads the design, development, and scaling of machine learning systems for signals, attribution, recommendation, ranking, and advertising measurement. Provides technical direction, improves ML infrastructure, collaborates with cross-functional leadership, and promotes scalable, reliable, cost-effective engineering practices. The role requires shipping advanced models, mentoring engineers, solving ambiguous technical problems, and influencing Snap’s broader machine learning strategy.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Develop and deploy machine learning models and systems for time-series wearable sensing and brain-computer interface applications. Collaborate with research, software, and hardware teams to integrate algorithms, resolve issues, and evaluate model performance. Build training and testing tools, write high-quality code, communicate technical tradeoffs, and apply advanced ML techniques to physiological sensor data and real-time systems.