Machine Learning Engineer I

Global Sports Jobs Limited; Beaverton (OR); On-site; USD 120,000 - 160,000; Full time

Job summary

Nike Digital Channels’ Machine Learning/AI organization seeks an ML Engineer to build and deploy models that improve recommendations, ranking, and personalisation. You will own end-to-end ML workflows, from data exploration to production deployment, and collaborate with stakeholders across Product and Engineering.

The role emphasizes scalable pipelines, modern ML frameworks, and responsible AI practices within a cloud-native environment on AWS, Kubernetes, and related platforms.

Qualifications

• Bachelor's degree in information technology, computer science, or a related field; any suitable combination of education, experience, or training will be accepted.
• 1+ years' 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 the SDLC, including CI/CD, testing strategies (unit/integration plus LLM evals), and safe rollout patterns (feature flags, canaries, A/B tests).
• Ability to collaborate effectively across functions, demonstrating strong ownership, a collaborative mindset, and the ability to operate with urgency and integrity.
• 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.
• Strong understanding of AWS Cloud Services, Kubernetes, DevOps, Databricks, Python, and other cloud-native platforms.
• Passion for cloud-native development and platform ownership, with strong problem-solving ability and enthusiasm for cutting-edge technology.

Responsibilities

• Build and deploy machine learning models that improve recommendations, ranking, and personalisation, 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 such as PyTorch or TensorFlow to design, train, and optimise 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 to translate product questions into ML solutions.
• Take ownership of delivering high-quality solutions, seeing work through from insight to impact while balancing speed and rigour.
• Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment.

Skills

Communication

Agile mindset

Collaboration

AI fluency

Ownership

Education

Bachelor's degree in IT/CS or related field

Tools

Python

PyTorch

TensorFlow

Spark

Airflow

AWS

Kubernetes

Databricks

CI/CD

FastAPI

Job description

About the Role

This role sits within Global Technology's Machine Learning/AI organisation, a team that supports Nike Digital Channels by implementing innovative athlete-facing experiences. The engineer works directly with technical and non-technical Machine Learning/AI stakeholders, athletes, and other teams across Global Technology. The position focuses on cloud-native development and platform ownership, building and deploying machine learning models that improve recommendations, ranking, and personalisation. The role suits someone who thrives in a collaborative environment, is excited by cutting-edge technology, and excels at problem-solving.

Key Responsibilities

• Build and deploy machine learning models that improve recommendations, ranking, and personalisation, 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 such as PyTorch or TensorFlow to design, train, and optimise 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 to translate product questions into ML solutions.
• Take ownership of delivering high-quality solutions, seeing work through from insight to impact while balancing speed and rigour.
• Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment.

Required Experience & Qualifications

• Strong technical and non-technical communication skills, including translating business and research goals into technical requirements and explaining technical details to both technical and non-technical stakeholders.
• Bachelor's degree in information technology, computer science, or a related field; any suitable combination of education, experience, or training will be accepted.
• 1+ years' 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 the SDLC, including CI/CD, testing strategies (unit/integration plus LLM evals), and safe rollout patterns (feature flags, canaries, A/B tests).
• Ability to collaborate effectively across functions, demonstrating strong ownership, a collaborative mindset, and the ability to operate with urgency and integrity.
• 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.
• Strong understanding of AWS Cloud Services, Kubernetes, DevOps, Databricks, Python, and other cloud-native platforms.
• Passion for cloud-native development and platform ownership, with strong problem-solving ability and enthusiasm for cutting-edge technology.

Preferred Skills

• Experience with AWS SageMaker, Docker/containerisation, and FastAPI.
• Experience with inference optimisation (latency tuning, caching, embedding serving, and search ranking/information retrieval).

The Application Process

A number of accommodations are offered to complete the interview process, including screen readers, sign language interpreters, accessible and single-location in-person interviews, closed captioning, and other reasonable modifications as needed.