Job Opportunities API

About Slickdeals: We believe shopping should feel like winning. That’s why 10 million people come to Slickdeals to swap tips, upvote the best finds, and share the thrill of a great deal. Together, our community has saved more than $10 billion over the past 26 years. We’re profitable, passionate, and in the middle of an exciting evolution—transforming from the internet’s most trusted deal forum into the go-to daily shopping destination. If you thrive in a fast-moving, creative environment where ideas turn into impact fast, you’ll fit right in. The Purpose: The Personalization team owns the systems that decide what each Slickdeals user sees, from homepage and feed rankings to deal recommendations across the site and in lifecycle channels. Personalization is one of our highest-leverage investments: it directly drives engagement, retention, and revenue across tens of millions of monthly users. We’re hiring a Sr. ML Engineer II who can operate end-to-end across the recommendation stack. This is a true hybrid role with roughly half modeling and half infrastructure. You will design and ship recommendation models (retrieval, ranking, and re-ranking) and build the production ML systems that train, serve, and evaluate them at scale. You’ll work closely with data scientists, product engineers, and the Search & Discovery and Shopping Graph teams. You will be building products using technologies such as AWS SageMaker, PyTorch, TensorFlow, vector databases, Elasticsearch, HBase, SQS/Kafka, REST web services, LLMs, and more. What You'll Do: This role spans the full ML lifecycle for recommendations — from candidate generation through ranking, serving, and online evaluation. Concretely: Modeling Design, train, and ship recommendation models including two-tower / dual-encoder retrieval, neural ranking, and re-ranking models Build embedding pipelines for users, deals, merchants, and content; iterate on representation learning approaches Improve candidate generation strategies, including ANN-based retrieval over learned embeddings Define and run rigorous offline evaluation (recall@k, NDCG, MAP, calibration) and partner with data science to design online A/B tests Partner with product and data science on personalization surfaces — homepage, feeds, deal pages, search re-ranking, and lifecycle channels Infrastructure Build and own end-to-end ML pipelines for recommendations: data preparation, training, evaluation, deployment, and monitoring Design and operate low-latency model serving for high-QPS recommendation traffic Build feature pipelines and feature-store patterns that maintain online/offline parity Design, architect, and build reliability, observability, and utilization infrastructure for the recommendations stack Improve training cost, turnaround time, and reproducibility on the ML platform; collaborate with data scientists to unblock experimentation Cross-cutting Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence Collaborate with engineers within the team and across the company to solve complex data problems at scale Write high-quality, product-level code that is easy to maintain and test following standard methodologies What We're Looking For: 8+ years of relevant professional experience Demonstrated experience designing, training, and shipping recommendation systems in production — not just classifiers or general ML Hands-on experience with deep learning for recsys: two-tower / dual-encoder models, embedding-based retrieval, neural ranking, or similar Strong ML fundamentals: model evaluation methodology, A/B testing, debugging models at scale, handling data and label quality issues Proficiency with ML modeling frameworks (PyTorch and/or TensorFlow) (5+ yrs) Experience with model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton, or compara