Uber · San Francisco, CA, United States
Skills: Python, Java
About the Role
The UberEats Feed Ranking team is responsible for the ML ranking layer which personalizes the Eats Homefeed. This personalization layer is one of the main drivers of order growth across the Delivery organization. As a Machine Learning Engineer (MLE) on the Feed Ranking team, you will own and develop models across our entire ranking stack (Retrieval + Pre-Ranking, Second Pass Ranking, Post-Ranking). Our models are sophisticated (>5B parameters), our features are rich (near-realtime engagement history with 1-year lookback), and our serving requirements are stringent (>10M QPS with low latency and high availability). As an MLE, the reward for working within these requirements is significant - offline performance gains in our ranking models are strongly correlated with topline impact for the entire Eats business.
What You’ll Do
Own and develop on all parts of the ML lifecycle - both offline (training, model architecture, etc.) and online (serving efficiency, GPU provisioning + utilization, etc.). Bring SOTA ML techniques from DLRM and LLM literature (Scaling Laws, Kernel Fusion, Latent Attention, Agentic Auto-Research, RL-based post-ranking, etc.) into our modeling stack. Collaborate closely with backend engineers to ensure high-quality and reliability in end-to-end integration. Coordinate with Science and Product partners to maximize the business impact of ML improvements.
Basic Qualifications
Bachelor’s degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 3+ years of full-time experience working on production ML systems. Strong proficiency in at least one programming language such as Python, Go, or Java. Strong proficiency with common Deep-Learning frameworks (PyTorch, Tensorflow/Keras, Jax, etc.). Experience with distributed data processing and ETL frameworks (Spark, Ray Data, etc.). Strong technical communication and ability to manage multiple cross functional stakeholders.
Preferred Qualific …truncated… Deep Learning Re…
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