Lead Machine Learning Engineer - AI/ML

September 17

Apply Now

Receive Emails with Similar Jobs

Nike

WebsiteLinkedInAll Job Openings

Athletic Shoes • Apparel • Sports Equipment • Digital • Merchandising

10,000+

Description

• Apply machine learning, collaborative filtering, NLP, and deep learning methods to massive data. • Provide technical leadership, establishing standards and principles for your team to follow. • Support investigation of new software packages/tools, APIs, and algorithms to deliver quality analytics and machine learning at scale. • Collaborate with a cross functional agile team of software engineers, data engineers, ML experts, and others to build new product features. • Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring. • Write production-quality code for ML models as online services and APIs. • Present complex analyses clearly and concisely. • Ability to build collaborative relationships with peers and multi-functional partners. • Provide support by writing documentation and tutorials as well as providing guidance to users with a variety of technical skills.

Requirements

• Bachelor's Degree or higher in Statistics, Computer science or a combination of relevant education, training, and experience. • Advanced degrees a plus (PhD, Masters, etc.) • 5+ plus years of experience in enterprise environment with a combination of technology and team leadership responsibilities. • Expertise with Python, Spark, or Java. • Strong leadership skills to mentor and guide a team of Machine Learning Engineers. • Balance of technical expertise, strategic planning, and team management to ensure project execution and workforce optimization. • Expertise in building and productionalizing large scale consumer facing ML models. • Designed, built and shipped applications that scale and implementing best practices in ML Ops and CI/CD to build state of the art ML models. • Proficient at writing good quality, well-documented and tested, scalable code - Python preferred. • Experience with tools like mlFlow, Airflow, Docker and Cloud Platforms such as AWS/GCP is ideal. • Experience deploying, monitoring and maintaining data science products in cloud environments such as AWS. • Knowledge of techniques for model compression, quantization, and optimization for deployment in resource-constrained environments. • Experience with data processing and storage frameworks like S3, Spark, Dynamo, etc.