Machine Learning Engineer (Video Generation)

Apple

  • Beijing
  • Permanent
  • Full-time
  • 2 months ago
If you are the kind of people who are passionate on pursuing excellence, embracing challenges, enjoying work with others, learning new things along the way, Apple is the right place for you. The ideal candidate will possess the self-motivation, curiosity, and initiative to achieve those goals. Analogously, the candidate is a lifelong learner who passionately seeks to improve themselves and the quality of their work.DescriptionThe computer vision engineer will work in a dynamic team as part of the Video Engineering org which develops on-device computer vision and machine perception technologies across Apple's products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands.Minimum Qualifications
  • M.S. or Ph.D. in Electrical/Computer Engineering, Computer Science, Mathematics, Physics, or a related field, with a research focus on computer vision or data-centric machine learning.
  • Production-Scale Expertise: Demonstrated success designing and shipping petabyte-scale image/video data systems to production.
  • Domain Depth: Hands-on experience in at least one of the following areas: video generation pipelines, multimodal LLM training, or data-centric AI workflows.
  • Technical Stack: Proficient in Python and C++ or Rust, with production experience using at least one distributed data framework (e.g., Spark, Ray, Flink, Dask).
  • Communication & Collaboration: Exceptional written and verbal English skills; comfortable presenting to large technical audiences and partnering with cross-functional teams.
Preferred Qualifications
  • Deep familiarity with video-generation and multimodal foundation models, including the specialized data-loading strategies they demand.
  • Proven track record curating and serving 10 PB+ or 1 B-item+ datasets for machine-learning and computer-vision workloads, with an emphasis on reliability, privacy, and cost efficiency.
  • Publications or significant OSS contributions in scalable data systems, dataset retrieval/search, or data-centric AI-and active participation in relevant benchmarks, challenges, or steering committees
  • Hands-on mentality to own engineering projects from inception to shipping products and the ability to work independently and as part of a cross-functional team.
  • Track records of adopting ML to solve cross-disciplinary problems.
  • Team-oriented, self-motivated, and relentlessly focused on translating ambitious ideas into measurable impact.

Apple