Industrial Machine Learning Engineer

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple and help us leave the world better than we found it. Our team is responsible for the development of the inspection equipment that determines if a product meets Apple's high standards across all of the mechanical portion of hardware we make - iPhone, Mac, iPad, Watch and others.

- Collaborate with mechanical and quality engineers to apply machine learning and computer vision to industrial problems and manufacturing situations\n- Develop and deploy ML models for inspection equipment responsible for judging millions of units per day in challenging production environments\n- Identify opportunities in production and development processes to apply machine learning tools for improvements\n- Develop toolkits to guide application of machine learning combined with statistical tools for engineers\n- Assemble and analyze large data sets through SQL-based querying or development of scripts and code-modules to collate distributed and disparate data sources\n- Apply pattern detection and anomaly identification techniques to measures of interest\nProof-of-concept application of ML methods, Neural Networks, and Computer Vision for prescriptive/predictive applications\n- Develop software components in Python, Java, and/or C/C++/Obj-C towards roll-out of data automation systems\n- Work with 2D/3D triangulation laser systems and/or CCD inspection systems (Halcon, Keyence, Cognex, Visco)\n- Balance technical requirements while effectively managing collaborations with vendors to maintain schedule and ramp dates\n- Occasionally wear multiple hats: technical project manager, database specialist, and optics improvement specialist\n\n

12+ years of solid hands-on experience applying machine learning and/or computer vision techniques to build models integrated into industrial/manufacturing applications \nExperience with image processing and using ML tools to identify patterns in images, specifically applied to industrial or manufacturing environments\nExperienced user of machine learning and statistical-analysis libraries such as GraphLab Create, Turi Create, scikit-learn, scipy, PyTorch, Keras, NetworkX, Spacy, and NLTK\nStrong software development skills with proficiency in Python\nStrong working knowledge of ML algorithms including decision trees, probability networks, association rules, clustering, regression, neural networks, CNNs, and object detection\nFamiliarity with mechanical metrology system qualification processes (GRR, Correlation, Stability, Reliability)\nBasic understanding of manufacturing processes (CNC, modeling, laser welding, etc.)\nAbility to explain and present analyses and machine learning concepts to a broad technical audience\nAbility to travel internationally to manufacturing sites - 25-50%\nBS in a related engineering field\n

Experience with deep learning frameworks such as mxnet, Torch, Caffe, and TensorFlow\nExperience with cloud computing platforms (AWS) and deployment tools like Docker\nExperience building Software ML solutions from inception to production\nProficiency with CLI, Linux and Unix shell scripting\nData visualization, data analytics, and data mining experience\nInternational team leadership experience (academic or professional)\nKnowledge of basic networking concepts and protocols (TCP/IP, HTTP, etc.)\nUnderstanding of optics, image acquisition, software filtering and judgment algorithms\nIntermediate knowledge of automation including system layout, architecture, and cycle time optimization\nProven track record for self-study and self-exploration into new tools and techniques\nAbility to analyze existing database schema DDL/instance layout and determine migration impacts\nStrong interest in technical details while maintaining grasp of the big picture as it relates to overall product quality\nHigh level of autonomy and influence to unblock delivery of results across various teams\nApplied background in Hadoop, Spark, Hive, Cassandra, and knowledge of R is a plus\nStrong analytic problem-solving skills and aptitude for learning systems quickly\nCreative collaboration skills\nProficient use of English both written and oral\nMS in related engineering field