Principal Machine Learning Engineer

Nike • Beaverton, OR

1d

About The Position

We’re looking for a Principal Machine Learning Engineer to lead and elevate our machine learning capabilities within Nike’s Consumer Product & Innovation organization. This role is for a seasoned expert who thrives on building scalable ML solutions, mentoring others, and driving innovation from concept to production. You’ll own ML Ops strategy, guide technical direction, and ensure our models deliver impact at global scale. You bring deep experience across a range of machine learning models and algorithms, with a proven track record of deploying models into production environments. You’re a strategic thinker who can take a team vision, own a critical piece of it, and run with confidence—developing proactive solutions that meet business goals while inspiring and guiding other MLEs along the way. WHAT YOU WILL WORK ON You will: Own end-to-end ML Ops strategy and execution for high-impact initiatives. Design, build, and deploy scalable machine learning models across diverse domains, ensuring production-grade reliability and performance. Mentor and coach other Machine Learning Engineers, fostering technical excellence and growth. Translate business objectives into ML solutions, proactively developing options to achieve strategic goals. Experiment with and implement a broad range of algorithms, from classical models to deep learning architectures. Collaborate with product, engineering, and data science teams to integrate ML solutions into Nike’s product creation and merchandising ecosystem. Drive continuous improvement in model performance through rigorous evaluation, error analysis, and optimization. Champion best practices in ML Ops, including CI/CD pipelines, monitoring, and governance. WHO YOU WILL WORK WITH You’ll partner closely with peer machine learning engineers, data scientists, and product managers across Consumer Product & Innovation delivery teams. You’ll also collaborate with engineering and platform teams to scale ML solutions globally. WHAT YOU BRING PhD, Masters or Bachelors degree in Data Science, Applied Statistics, Software Engineering or related field. Will accept any suitable combination of education, experience and training.

Requirements

• PhD, Masters or Bachelors degree in Data Science, Applied Statistics, Software Engineering or related field. Will accept any suitable combination of education, experience and training.
• 8–10+ years of experience in machine learning engineering, with increasing leadership responsibilities (years may vary based on advanced degrees).
• Proven experience deploying ML models into production environments at scale.
• Expertise in ML Ops practices, including automation, monitoring, and lifecycle management.
• Strong foundation in machine learning algorithms and statistical methods, with exposure to a wide range of techniques.
• Proficiency in Python, SQL, and modern ML frameworks (e.g., TensorFlow, PyTorch, MLFlow, SageMaker).
• Experience with cloud-based ML platforms and distributed computing tools (e.g., Databricks, PySpark).
• Ability to mentor and lead technical teams, fostering collaboration and innovation.
• Excellent communication skills—able to simplify complex concepts for diverse audiences.

Nice To Haves

• Advanced degree in Computer Science, Machine Learning, or related field preferred.

Responsibilities

• Own end-to-end ML Ops strategy and execution for high-impact initiatives.
• Design, build, and deploy scalable machine learning models across diverse domains, ensuring production-grade reliability and performance.
• Mentor and coach other Machine Learning Engineers, fostering technical excellence and growth.
• Translate business objectives into ML solutions, proactively developing options to achieve strategic goals.
• Experiment with and implement a broad range of algorithms, from classical models to deep learning architectures.
• Collaborate with product, engineering, and data science teams to integrate ML solutions into Nike’s product creation and merchandising ecosystem.
• Drive continuous improvement in model performance through rigorous evaluation, error analysis, and optimization.
• Champion best practices in ML Ops, including CI/CD pipelines, monitoring, and governance.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees