Machine Learning Engineer I

Nike • Beaverton, OR

21h

About The Position

As part of Global Technology’s Machine Learning/AI organization, this role supports Nike Digital Channels by implementing athlete-facing experiences. The Machine Learning Engineer works with technical and non-technical Machine Learning/AI stakeholders, athletes, and teams across Global Technology. The position focuses on building, evaluating, deploying, and maintaining production machine learning models and pipelines for recommendations, ranking, and personalization, while contributing to experimentation and responsible AI practices. The role also calls for cloud-native development and platform ownership, including production-grade LLM and agent workflows and integration of LLM capabilities into existing services and development processes.

Requirements

• Bachelor’s degree in information technology, computer science, or a related field; suitable combinations of education, experience, or training will be accepted.
• At least 1 year of experience building and deploying machine learning models in production environments.
• Proficiency in Python and experience with at least one major machine learning framework, such as PyTorch or TensorFlow.
• Experience working with data pipelines and distributed systems, such as Spark or Airflow, to support ML workflows.
• Experience designing and delivering production-grade LLM or agent workflows, including tool/function calling, orchestration patterns, and retrieval-augmented generation (RAG).
• Experience integrating LLM capabilities into existing services and software development life cycles, including CI/CD, unit and integration testing, LLM evaluations, and safe rollout patterns such as feature flags, canaries, or A/B tests.
• Strong technical and non-technical communication skills.
• Ability to collaborate across functions, demonstrate ownership, and adapt approaches based on data and changing priorities while remaining focused on outcomes.
• Ability to independently apply machine learning techniques and emerging tools, including LLMs, to solve problems responsibly.

Nice To Haves

• Experience with AWS SageMaker.
• Experience with Docker or containerization.
• Experience with FastAPI.
• Experience with inference optimization, including latency tuning, caching, embedding serving, and search ranking or information retrieval.
• Understanding of AWS Cloud Services, Kubernetes, DevOps, Databricks, Python, and other cloud-native platforms.

Responsibilities

• Build and deploy machine learning models to improve recommendations, ranking, personalization, user experience, and engagement.
• Own problems end-to-end, from data exploration and feature engineering through model training, evaluation, and production deployment.
• Develop and maintain scalable ML pipelines using tools such as Spark and Airflow.
• Apply modern ML frameworks such as PyTorch or TensorFlow to design, train, and optimize production models.
• Contribute to experimentation frameworks, including A/B testing and offline evaluation, to improve model performance.
• Collaborate with Product and Engineering to translate product questions into machine learning solutions.
• Deliver high-quality solutions from insight through impact, balancing speed and rigor.
• Apply responsible AI practices, considering fairness, transparency, and safety in model development and deployment.
• Translate business and research goals into technical requirements and communicate technical details to technical and non-technical stakeholders.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level