Machine Learning Systems Engineer at Apple

Expires in 13 days

Santa Clara Valley (Cupertino), California, United States
Machine Learning and AI


Posted: Dec 2, 2020
Role Number:200209701
The Video Computer Vision org is a centralized applied research and engineering organization responsible for developing real-time on-device Computer Vision and Machine Perception technologies across Apple products. We balance research and product to deliver Apple quality, state-of-the-art experiences, innovating through the full stack, and partnering with HW, SW and ML teams to influence the sensor and silicon roadmap that brings our vision to life. Examples include FaceID, Animoji/Memoji, Scene Understanding, People Understanding and Positional Tracking (VIO/SLAM).

Key Qualifications Key Qualifications
  • Solid experience building distributed systems
  • Solid programming skills
  • Strong Familiarity with AWS services such as EC2, SNS, SQS,S3 and RedShift
  • Experience with SQL and Non-SQL Databases: Postgres, Elasticsearch, Redis
  • Full-stack Web development skills
  • Experience with version control, artifact repositories
  • Experience or interest in gaining experience with AWS SageMaker, PyTorch Serve, Tensorflow Serving
  • Experience or interest in gaining experience working with Machine Learning and data driven AI Teams

Description Description
Machine Learning Systems engineer will have a strong emphasis on large-scale Machine Learning and data curation. The goal is to develop crowdsourcing infrastructures and control policies for closing the loop between machine learning and large scale human annotation. This position will require interfacing with multiple teams including machine learning, data scientists and cloud infrastructure support teams. You will work together with similar minds in a unique development team where your skills and expertise will be applied to Apple products. - Develop crowdsource infrastructure for labeling images/videos using a large number of labelers distributed across multiple international facilities. - Develop APIs and SDKs for interfacing between labeling architecture and machine learning teams. - Develop Control Systems to prioritize, schedule and distribute labeling tasks to different pools of workers at different time scales and security levels. - Develop the Software infrastructure to monitor quality, costs and make projections across a large number of simultaneous data gathering projects. - Develop tools for annotating images and videos.

Education & Experience Education & Experience
PhD or Masters in Computer Science, Machine Learning, Computer Vision or similar, alternatively a comparable industry career, with significant experience in large-scale data analytics and infrastructure.

Additional Requirements Additional Requirements

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