Machine Learning Engineer I
Nike Beaverton, Oregon, United States
Retail · 10,001+ employees
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About the role
Build and deploy machine learning models to improve recommendations, ranking, and personalization for Nike digital channels. Manage end-to-end ML workflows including data exploration, feature engineering, model training, and production deployment.
What they look for
Requirements
Requires a bachelor's degree in information technology, computer science, or a related field. Candidates must have at least 1 year of experience building and deploying machine learning models in production environments using Python and major ML frameworks.
Benefits
Full description
WHO YOU’LL WORK WITH
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, athlete*s, and other teams within Global Technology.
WHO WE ARE LOOKING FOR
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.
To be considered for this position, the candidate should:
- Have 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
WHAT YOU’LL WORK ON
- 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
We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.
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