Lead Machine Learning Engineer

Key Skills

Aartificial intelligence, Clustering, CNNs, Java Programming, Machine learning techniques, Natural Language Processing (NLP), OpenCV, Python Programming, Scala Programming, Scikit-learn, SPARK Programming, SparkML, SQL, TensorFlow

Job Description

NIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At NIKE, Inc. it’s about each person bringing skills and passion to a challenging and constantly evolving game.

Lead Machine Learning Engineer–NIKE, Inc., San Francisco, CA. Develop and program integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments. Develop and communicate descriptive, diagnostic, predictive and prescriptive insights/algorithms. Use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others. Develop and evaluate algorithms to improve product system performance, quality, data management and accuracy. Use current programming language, technologies to translate algorithms and technical specifications into code. Complete programming and implement efficiencies, perform testing and debugging. Complete documentation and procedures for installation and maintenance. Apply deep learning technologies to give computers the capability to visualize, learn and respond to complex situations. Adapt machine learning to areas such as virtual reality, augmented reality, artificial intelligence, robotics and other products that allow users to have an interactive experience. Work with large scale computing frameworks, data analysis systems and modeling environments. Telecommuting is available from anywhere in the U.S., except from AK, AL, AR, DE, HI, IA, ID, IN, KS, KY, LA, MT, ND, NE, NH, NM, NV, OH, OK, RI, SD, VT, WV, and WY.

Employer will accept a Master’s Degree in Computer Science, Statistics, Computer Information Systems, or Engineering and 2 years of experience OR a Ph.D. in Computer Science, Statistics, Computer Information Systems, or Engineering and completion of a Ph.D-level course, research project, internship, or thesis or one year of experience in the job offered or engineering-related occupation.

• Python, Spark, Java, Scala;
• AI/ML techniques, including neural networks, tree ensembles, regressions, and hypothesis testing;
• Data and Machine learning pipelines using Scikit-learn, Tensorflow, Spark ML (MLlib), Open CV;
• SQL, and data query languages;
• Architecting or delivering cloud solutions such, as Google cloud or AWS or similar platforms;
• Algorithms, such as ALS, Neural Nets, CNN, Clustering, personalization techniques, time series, NLP and image modeling;
• End-to-end lifecycle of machine learning systems or Machine learning engineering development;
• Delivering and supporting high impact solutions that scale across very large datasets to production;
• Track recording of delivering and supporting high impact solutions that scale across very large datasets to production;
• Lead and mentor team members;
• Partnering across product, architecture and engineering
• Lifecycle of model development from experimentation to production and measurement.

Rate of Pay: $183800 to $262037.30 per Year

LI-DNI

NIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete* in the world.

NIKE, Inc. is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.