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Senior ML Software Engineer - Large Scale Spatial/Temporal Data Processing at Apple
Cupertino, California, United States -
Full Time

Start Date

Immediate

Expiry Date

13 Feb, 26

Salary

0.0

Posted On

15 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Programming, PyTorch, TensorFlow, Scikit-learn, Scala, Python, Java, C++, Spark, Solr/Lucene, Cassandra, Cloud Platforms, Problem Solving, Communication, Collaboration

Industry

Computers and Electronics Manufacturing

Description
Are you searching for an opportunity that will challenge, encourage and make you proud? If so, our team would like to meet you. The Maps Places Data team uses a variety of spatiotemporal signals to improve and maintain the quality of all Places of Interest in the global maps database. We’re looking for an ML engineer with the curiosity and expertise to create the next generation of algorithms and processes to solve these challenging problems. DESCRIPTION Our team is responsible for the accuracy of a variety of attributes for the Points of Interest in the map database. We produce algorithms and processes that can operate globally, leveraging all available signals from aerial imagery to users’ feedback. MINIMUM QUALIFICATIONS 7+ years of experience in building large scale machine learning systems. Strong programming skills and hands-on experience with machine learning tools and libraries such as PyTorch, TensorFlow, Scikit-learn; programming skills in Scala, Python, Java, or C++ Knowledge of Spark, Solr/Lucene, Cassandra, and related big data technologies Metrics focused and passionate about delivering models that render high quality results Familiarity with cloud platforms such as AWS, GCP, or Azure. Strong problem-solving, communication, and ability to collaborate with cross-functional teams. Solid track record of delivering complex ML-powered features. PREFERRED QUALIFICATIONS Strong spatial aptitude and intuition for algorithm design in the mapping domain Experience with classical computer vision techniques and modern solutions based on foundation models
Responsibilities
The team is responsible for the accuracy of various attributes for Points of Interest in the map database. They produce algorithms and processes that operate globally, leveraging signals from aerial imagery and user feedback.