Lead Machine Learning Engineer AI/ML - Consumer Marketplace (Remote Work Option)

Nike - Beaverton, OR

Full-time - Mid Level

Remote - Beaverton, OR

Leather and Allied Product Manufacturing

About the position

Nike is seeking a Lead Machine Learning Engineer to join the Enterprise Data & AI Consumer AI/ML team. This role focuses on building solutions that enhance the customer experience on the Nike Digital Platform. The ideal candidate will be a problem-solver, eager to learn new technologies, and capable of collaborating with a cross-functional team to develop features quickly while maintaining quality. This position plays a crucial role in accelerating Nike's mission of serving athletes.

Responsibilities

• Apply machine learning, collaborative filtering, NLP, and deep learning methods to massive data.
• Provide technical leadership, establishing standards and principles for the team.
• Support investigation of new software packages/tools, APIs, and algorithms for quality analytics and machine learning at scale.
• Collaborate with a cross-functional agile team 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.
• Build collaborative relationships with peers and multi-functional partners.
• Write documentation and tutorials and provide guidance to users with varying technical skills.

Requirements

• Bachelor's Degree or higher in Statistics, Computer Science, or a combination of relevant education, training, and experience.
• 5+ years of experience in an enterprise environment with technology and team leadership responsibilities.
• Expertise with Python, Spark, or Java.
• Strong leadership skills to mentor and guide a team of Machine Learning Engineers.
• Expertise in building and productionalizing large scale consumer-facing ML models.
• Experience designing, building, and shipping applications that scale and implementing best practices in ML Ops and CI/CD.
• Proficient at writing good quality, well-documented, and tested scalable code, preferably in Python.
• Experience with tools like mlFlow, Airflow, Docker, and Cloud Platforms such as AWS/GCP.
• 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.

Nice-to-haves

• Advanced degrees (PhD, Masters, etc.) are a plus.

Benefits

• Health insurance
• Dental insurance
• 401k
• Paid holidays
• Flexible scheduling
• Professional development
• Employee discount programs