Software Engineer, ML Infrastructure, Content Signal & ...

Snap Inc ( is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Companyu2019s three core products are Snapchat (, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio (, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles (. Snap Engineering ( teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. Weu2019re deeply committed to the well-being of everyone in our global community, which is why our values ( are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. Youu2019ll play a critical role in scaling our Content Signal & Training Data infrastructure, developing new signals for ranking and retrieval, optimizing training data pipelines, and driving innovations that make Snapchatu2019s ranking and recommendation systems more reliable, efficient, and impactful. Weu2019re looking for a Software Engineer, Content Signal & Training Data Infrastructure to join Snap Inc What youu2019ll do: + Design and optimize systems for large-scale signal generation, indexing, serving, and applicationsBuild and maintain content feature lifecycle management, including generation, storage, sourcing, monitoring, and deprecation of unused features + Simplify the content feature development process by collaborating with ML data platform teams and improving tooling for generation, storage, and sourcing + Optimize and monitor signal pipelines for reliability, latency, and scalability + Develop infrastructure for training data pipelines, including logjoin optimization, streaming logjoin, data sampling, data shuffling, and window tuning + Build and maintain training data for new applications and ranking models, including experiments on long-term objectives such as user retention and creator affinity + Collaborate with ML engineers to improve training workflows (feature engineering, preprocessing, model iterations, evaluation, and inference) + Build training data monitoring and analysis tools with Bento and data infra teams, including SQL-based analysis, feature importance, discrepancy detection, and anomaly detection Knowledge, Skills & Abilities: + Strong programming skills in Python, Java, Scala, or C++Strong problem-solving skills with a focus on system performance, data quality, and scalability + Deep understanding of distributed systems, data pipelines, and ML infrastructure + Experience with big data processing frameworks such as Spark, Flink, Dataflow, or Ray + Familiarity with feature engineering, signal pipelines, and model training workflowsProven track record of operating highly available and reliable infrastructure at scale + Ability to proactively learn new concepts and apply them in a fast-paced environment + Strong collaboration skills with ML engineers, data scientists, and infra teams Minimum Qualifications: + Bacheloru2019s degree in a technical field such as computer science or equivalent experience + 6+ years of post-Bacheloru2019s software development experience; or Masteru2019s degree in a technical field + 5+ years of post-grad software development experience; or PhD in a relevant technical field + 2+ years of post-grad software development experience + Experience building large-scale data or ML production systems, distributed systems, or big data processing Preferred Qualifications: + Masters/PhD in a technical field such as computer science or equivalent industry experience + Experience with feature platforms, logjoin optimization, and training data systems + Familiarity with ML frameworks such as TensorFlow, PyTorch, or Spark ML + Experience with signal pipelines, feature registries, retrieval systems, and data quality monitoring + Hands-on experience with Snapu2019s internal tech stacks such as Robusta, Hashi, Dataflow, Feature Registry, Mixer, Retrieval Service, logjoin, and dcoll If you have a disability or special need that requires accommodation, please donu2019t be shy and provide us some information (.

Created: 2025-09-25