Machine Learning Engineer

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.

• 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 Tensor Flow 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.

• 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, Tensor Flow).
• 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, Dev Ops, 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.

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

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.