Machine Learning / Data Systems Engineer
Santa Clara Valley (Cupertino), California, United States
Machine Learning and AI
Develop infrastructure and algorithms for continuous integration of Machine Learning and large scale media annotation. You will work with a unique development team where your skills and expertise will be applied to the next generation of Apple products.
- Experience with Event-driven Micro Service pipelines
- Experience with Publish/Subscribe Infrastructure like Kafka
- Strong Familiarity with AWS services such as EC2, SNS, SQS, Kinesis.
- Solid Python and C++ programming skills
- Experience with CrowdSourcing Platforms such as Mechanical Turk
- Experience with SQL and Non-SQL Database Coding
- Full-stack Web development skills
- Experience with version control, artifact repositories
- Strong Interest in Machine Learning
- Develop micro services based infrastructures for large scale machine learning
- 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
Machine Learning/ Data Systems engineer with a strong emphasis on large-scale Machine Learning and data infrastructure. 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 a cloud and 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.
Education & Experience
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.