Location: Cupertino, CA
Position: Machine Learning-Industrial Methods Engineer
In Manufacturing Design, the team takes a vision of a product and turns it into a reality through development of mass production processes and systems. To do this, there are many large problems that the team needs to tackle, and the Industrial Methods Team jumps right in. Using statistics and the scientific process, the team is able to solve the toughest problems. As an Industrial Methods Engineer, you would be using your experience with Machine Learning tools to apply them to problems of industrial scope and mass production scale. In this highly visible role, you will have the opportunity to make a significant impact on Apple Products, and leave a footprint for years to come.
- Minimum four years of solid hands-on experience applying machine learning techniques to build models integrated into Industrial applications. Additional experience with data visualization, data analytics and data mining.
- Strong analytic problem solving skills, and an aptitude for learning systems quickly.
- Strong software development skills with proficiency in Python (applied background in Hadoop, Spark, Hive, Cassandra, and knowledge of R is a plus).
- Experienced user of machine learning and statistical-analysis libraries, such as GraphLab Create, scikit-learn, scipy, NetworkX, Spacy, and NLTK
- Experience with deep learning frameworks, such as mxnet, Torch, Caffe, and TensorFlow
- Strong working knowledge of ML algorithms including decision trees, probability networks, association rules, clustering, regression, and neural networks.
- High level of autonomy and influence to unblock delivery of results (evaluate and solve difficult problems involving various teams ranging from data instrumentation to analytics tool development).
- A proven track record for self study and self exploration into new tools and techniques
- Ability to analyze existing database schema DDL/instance layout and determine impacts migrating to new target schema/instance environment.
- Ability to explain and present analyses machine learning concepts to a broad technical audience.
- Collaborate with mechanical and quality engineers to apply machine learning to industrial problems and situations
- Identify opportunities in the production and development processes to apply machine learning tools for improvements
- Develop a toolkit to guide application of machine learning tools combined with statistical tools for common engineers
- Assemble large data sets for analysis either through direct SQL-based querying or development of scripts and code-modules to collate distributed and disparate data sources
- Analyze large data sets towards the goal of identifying anomalies (pattern detection) and variabilities in a measure of interest
- Proof-of-concept application of ML methods and Neural Networks for a wide range of prescriptive/predictive applications
- Develops software components in Python, Java and/or C/C++/Obj-C towards roll-out of a data automation system
- Travel internationally to manufacturing sites – 30%
- Bachelors, Masters, or PhD in Data Science, Machine Learning, Statistics, Data Analytics and 3+ years of application in industry or academic fields
- Bachelors, Master, or PhD in an Engineering (Mechanical, Industrial, Chemical, other) plus self study of Machine Learning and Statistical toolsets and 5+ years of application in industry
Apple is an Equal Employment Opportunity Employer that is committed to inclusion and diversity. We also take affirmative action to offer employment and advancement opportunities to all applicants, including minorities, women, protected veterans, and individuals with disabilities.