Machine Learning Engineer

Join the Apple Maps team and help shape the future of navigation for hundreds of millions of users worldwide We are seeking a dedicated ML Engineer to revolutionize our traffic predictions and navigation systems. Your work will directly impact how people move through their world, making their journeys more efficient and enjoyable. This is an ideal position for an engineer with a strong background in supervised, unsupervised, and deep learning techniques who possesses deep intuition and experience in applying statistical modeling techniques to user facing products. If you're passionate about applying ground breaking machine learning techniques to solve sophisticated, real-world problems at a global scale, this is your opportunity to make a difference

Description

As an ML Engineer on the Apple Maps Navigation team, you'll be at the forefront of developing and optimizing machine learning models that power our traffic predictions and navigation products. Your work will ensure that Apple Maps provides the most accurate and reliable navigation experience possible, while handling and working with high volumes of live and historical data. You'll work as part of a dynamic, multi-functional team of software and ML engineers, data scientists, and traffic experts to help set the future direction of the product. Your responsibilities will include prototyping domain-specific algorithms, rapidly evaluating their quality and leading the production implementation effort. Our collaborative environment encourages knowledge sharing and provides opportunities to work on various aspects of Apple Maps. ","responsibilities":"Designing, implementing, and maintaining algorithms and ML models for traffic prediction to support navigation decisions

Creating new technical capabilities that serve as building blocks for new and improved features that surprise and delight our customers

Working across multiple engineering, data science, UX, and product teams to help set the future direction of the product

Collaborating with data engineers to process and analyze extensive streams of location data in privacy-preserving fashion

Developing and improving ML pipelines for model training, evaluation, and deployment in real-time and batch environments

Continuously monitoring and improving model performance through experimentation and analysis

Leading the iterative and data-driven research and exploration of new approaches for existing or new areas of consideration

Communicating timelines and setting expectations with others under uncertainty

Preferred Qualifications

MS/PhD or equivalent experience in Computer Science, Machine Learning, or related fields

Proficiency in Scala, Java, and/or C++

Proficiency in working with SQL/NoSQL databases

Excellent communication skills and ability to adapt quickly in a dynamic, fast-paced environment

Consistent record in machine learning, validated through relevant industry experiences and/or publications in premier conferences or journals

Experience with large-scale data processing systems (e.g., Spark, Hadoop)

Experience with geospatial data analysis and modeling or transportation science

Domain expertise in transportation, navigation, or time series prediction

Experience with cloud technologies and distributed systems

Minimum Qualifications

BS in Computer Science, Machine Learning, or related fields

Strong programming skills in Python, with experience in ML frameworks such as PyTorch or Tensorflow

Experience with serving and deploying ML models at scale

Excellent problem solving and analytical skills, valuing a scientific approach by using experimentation and critical thinking to drive and validate high-quality results

At Apple, we're not all the same. And that's our greatest strength. We draw on the differences in who we are, what we've experienced, and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. We will work with applicants to make any reasonable accommodations.