Lead Machine Learning Engineer (AI/ML), ITC

The Lead AI/ML Engineer will influence and develop robust machine learning and generative AI solutions that have a direct impact on the business. The role is part of the Corporate Functions Artificial Intelligence team at Nike, which focuses on delivering AI capabilities for Nike's corporate functions. The team is globally distributed and works on machine learning problems at scale.

• Own projects end-to-end from conception to operationalization, demonstrating command of the full software development lifecycle.
• Set the technical direction for the team, provide vision and guidance to teammates, and raise the bar on engineering quality.
• Design and implement scalable applications that leverage prediction models and optimization programs to deliver data-driven decisions.
• Contribute to core advanced analytics, machine learning, and generative AI platforms and tools to enable prediction and optimization model development.
• Develop and share new skills, mentor, and contribute knowledge and software back to the analytics and engineering communities within Nike and at large.
• Work closely with business stakeholders, product owners, and engineering peers to ensure successful delivery of solutions.
• Work with other technology teams that lead upstream and downstream solutions to coordinate dependencies.

• Undergraduate degree in Computer Science, a Master's degree in a related engineering field, or equivalent experience.
• 8+ years of professional experience in software engineering.
• 3+ years of experience in the field of Machine Learning Engineering or related fields.
• A demonstrable history of technical leadership, mentoring engineers, and delivering value in an agile product model.
• Strong analytical mindset and experience leading others in problem solving.
• Proficiency working in a team and mentoring others to write robust, maintainable, and extendable code in Python; containerized in Docker, and automated with CI/CD.
• Expertise with agile development and test-driven development.
• Expertise with data structures, data modeling and software architecture.
• Expertise in producing predictive or mathematical optimization models and deploying them to production.
• Expertise in MLOps and an ability to articulate the role of MLOps in the machine learning development lifecycle from experimentation to production and measurement.
• Familiarity with frameworks such as Scikit-learn, PyTorch, TensorFlow, Spark, FastAPI or similar platforms and frameworks.
• Experience with complex data sets, ETL pipelines, SQL, and general data engineering.
• Expertise with cloud architecture and technologies, especially Amazon Web Services: ECR, SageMaker, Lambda, API Gateway.
• Familiarity with pipeline orchestration tools such as Airflow or Databricks Workflows.
• Experience with database technology (e.g. Postgres, Redis) and data processing technology (e.g. SageMaker or Databricks).

• Expertise with Spark, Kubernetes, Docker, Jenkins, Databricks, or Terraform is highly desirable.

is globally distributed and works on machine learning problems at scale.