Machine Learning Engineer I

Nike • Beaverton, OR

2d

About The Position

Working as part of Global Technology’s Machine Learning/AI organization, our team supports the Nike Digital Channels by implementing innovative athlete facing experiences. You’ll work directly with technical and non-technical Machine Learning/AI stakeholders, athletes, and other teams within Global Technology. We are looking for an ambitious Software Engineer I – Machine Learning/AI with a passion for cloud-native development and platform ownership. You are someone who thrives in a collaborative environment, is excited by cutting-edge technology, and excels at problem-solving. You have a strong understanding of AWS Cloud Services, Kubernetes, DevOps, Databricks, Python and other cloud-native platforms. You should be an excellent communicator, able to explain technical details to both technical and non-technical stakeholders and operate with urgency and integrity.

Requirements

• Strong technical and non-technical communication skills, including translating business and research goals into technical requirements.
• Bachelor’s degree in information technology, computer science or related field. Will accept any suitable combination of education, experience or training.
• 1+ years’ experience building and deploying machine learning models in production environments with Proficiency in Python and experience with at least one major ML framework (e.g. PyTorch, TensorFlow).
• Strong experience building and deploying machine learning models in production environments
• Proficiency in Python and experience with at least one major ML framework (e.g. PyTorch, TensorFlow).
• Experience working with data pipelines and distributed systems (e.g. Spark, Airflow) to support ML workflows
• Experience designing and delivering production-grade LLM/agent workflows, including tool/function calling, orchestration patterns, and retrieval-augmented generation (RAG).
• Experience integrating LLM capabilities into existing services and SDLC, including CI/CD, testing strategies (unit/integration + LLM evals), and safe rollout patterns (feature flags, canaries, A/B tests)
• Ability to collaborate effectively across functions, demonstrating strong ownership and a collaborative mindset
• Demonstrates an agile mindset, adapting approaches based on data and evolving priorities while maintaining focus on outcomes
• Growing AI fluency, with the ability to independently apply ML techniques and emerging tools (including LLMs) to solve problems responsibly.
• Experience with AWS SageMaker, Docker/containerization, FastAPI.
• Experience with inference optimization (latency tuning, caching, embedding serving, and search ranking/information retrieval)

Responsibilities

• Build and deploy machine learning models that improve recommendations, ranking, and personalization, driving measurable impact on user experience and engagement.
• Own problems end-to-end, from data exploration and feature engineering through to model training, evaluation, and production deployment.
• Develop and maintain scalable ML pipelines using tools such as Spark and Airflow to support reliable, high-quality model delivery.
• Apply modern ML frameworks (e.g. PyTorch or TensorFlow) to design, train, and optimize models in production environments.
• Contribute to experimentation frameworks, including A/B testing and offline evaluation, to iterate on model performance with an agile mindset.
• Collaborate cross-functionally with Product and Engineering, working with purpose to translate product questions into ML solutions.
• Take ownership of delivering high-quality solutions and see work through from insight to impact, balancing speed and rigor.
• Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Associate degree