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Machine Learning Data Engineer

Apple, Inc.
United States, Texas, Austin
July 26, 2022
Posted: Jun 29, 2022
Weekly Hours: 40
Role Number: 200322127
Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! The Infrastructure Services Software Development team is looking for a passionate, self-motivated and hard-working engineer to be part of a diverse, fast paced and high-energy team! We are seeking an enthusiastic and dedicated Data Engineer who is passionate about Machine Learning initiatives, to build high quality, scalable and resilient distributed systems that power the analytics platform and data pipelines. Be responsible for developing some of the key components of the platform, collaborate with cross-functional teams. Contribute to key and innovative technology which supports major Apple applications, with all the scalability and high-availability requirements that entails.
Key Qualifications
  • You are a core contributor on ML projects with a focus on data ingestion, transformation and presentation for ML apps and reporting.
  • Passionate and dedicated track record of designing and implementing scalable, performant data pipelines, data services, and data products.
  • This is a hands-on position, expect to write more code.
  • Proficiency in at least one programming language (Java, Python or Scala) and a tried understanding of SQL.
  • Previous experience of dealing with multivariate data at petabyte scale, especially in the time-series domain.
  • Be able to communicate collaboratively with Data Scientists and ML Software developers to understand requirements we have and deliver best in class data platform.
  • Previous experience with statistical modeling and deep learning frameworks / libraries we use is required.
  • Strong aptitude for learning new technologies related to Data Management and Data Science.
  • Proven record to create and perform independently and within a fast-paced, team-oriented environment.
  • Work with structured and unstructured data. Perform data cleansing, scraping unstructured data and converting into structured data.
  • Evaluate, benchmark and improve the scalability, robustness, efficiency and performance of big data platform and applications.
  • Experience with Kubernetes, Docker is a plus
In this role, handle implementing data pipelines focused on Machine Learning applications. You will develop data sets for POCs to demonstrate new insights. Several of these may lead to fully operational ML models and deploy and own the life-cycle on in-house and third party cloud environments. You will partner with various cross functional teams to define, develop and implement data technology solutions, with an emphasis on providing superior foundational data for ML applications. A strong understanding of distributed data systems and experience in using open source frameworks to build applications is required. A solid understanding of Deep learning platforms such as Keras, Tensor-flow and/or PyTorch is highly desirable, as is an ability to deploy solutions based on these platforms. Leveraging GPU & CPU resources as appropriate / understanding capacity requirements for ML Workloads, and working with partner teams to ensure scalability, business continuity and appropriate turnaround time is a key part of the operationalization effort. As a member of the team, you will be expected to take ownership of individual platform components and help set the vision and architecture for those. In the process, you will identify the requirements of new features, and propose design and drive the solution. A strong understanding of data governance and data privacy is expected for this role in keeping with Apple's strong commitment to the same.
Education & Experience
B.S or M.S in Computer Science, Mathematics, Statistics, Operational Research, Data Science / equivalent experience.
Additional Requirements
  • 3+ years of proven experience with Kubernetes, Docker is a plus