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In this concise recap of Morgan's (程墨) talk at Peking University, a veteran software leader shares six career stories, key reflective questions, and practical insights on leaving jobs, becoming a senior engineer, aligning with company values, leading teams, and the essential role of integrity.
On November 7, 2020, Hulu’s Shulei presented how the company’s video advertising platform employs AI-driven algorithms—including binge‑watch prediction, inventory forecasting with Prophet, and a PID‑controller based real‑time allocation system—to enhance targeting, optimize revenue, and improve user experience.
The Hulu AI Class event showcased a series of technical talks covering large‑scale graph neural network optimizations, multi‑factor video ad placement algorithms, recommendation and search engine techniques, machine‑learning‑driven video codec improvements, and advanced content‑embedding methods, highlighting practical engineering experiences from Hulu’s Beijing office.
This article walks you through Hulu's 2021 campus recruitment update, explains the online programming test schedule and format, shares interview tips, and provides detailed statements, sample I/O, and solution analyses for four algorithmic problems covering probability, array maxima, maze navigation, and segment‑tree optimization.
The Hulu AI team’s ICLR‑2020 paper introduces a consciousness‑prior‑driven graph neural network that dynamically prunes message‑passing subgraphs, achieving state‑of‑the‑art results on large‑scale knowledge‑graph completion tasks while improving interpretability and computational efficiency.
The CBVRP (Content-Based Video Relevance Prediction) challenge, co‑hosted by Hulu and ACM MM 2019, showcased the shift from user‑based collaborative filtering to content‑driven recommendation, highlighted winning teams and their papers, and underscored the ongoing research importance of cold‑start video recommendation for streaming platforms.
At a Tsinghua University forum, Hulu presented a comprehensive overview of its big‑data solutions for advertising and streaming, covering challenges of massive, complex data, the limits of MySQL, and advanced techniques using HBase, Protobuf, Redis batch pipelines, and its own MPP engine Nesto for high‑performance, scalable analytics.
This article provides a concise overview of Hulu's 2020 campus recruitment, including company FAQs, detailed descriptions of four programming problems with input/output specifications, sample cases, and step‑by‑step solution analyses to help candidates prepare for the online coding test.
This article reviews why deep neural networks are over‑parameterized, outlines the challenges of deploying them on mobile and embedded devices, and presents six major strategies—pruning, low‑rank approximation, filter selection, quantization, knowledge distillation, and novel architecture design—to accelerate and compress models while preserving performance.
This article reviews the evolution of autonomous‑driving research, explains how to design end‑to‑end deep‑neural‑network models such as PilotNet, and outlines a reinforcement‑learning based decision system, highlighting key architectures, performance metrics, and future challenges.
