Machine Learning Engineer I

Posted Yesterday
Beaverton, OR, USA
In-Office
Junior
Other • Retail
At NIKE, Inc., technology is laying the foundation for our digital transformation and direct-to-consumer strategy.
The Role
Build, deploy, and optimize machine learning models for recommendations, ranking, and personalization. Own the full ML lifecycle from data exploration and feature engineering through production deployment. Develop scalable pipelines with Spark and Airflow, apply PyTorch or TensorFlow, support experimentation and A/B testing, and integrate LLM and agent workflows. Collaborate with Product and Engineering teams while applying responsible AI practices and optimizing inference performance.
Summary Generated by Built In

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.

Skills Required

  • Bachelor's degree in information technology, computer science, or a related field, or a suitable combination of education, experience, or training
  • At least 1 year of experience building and deploying machine learning models in production environments
  • Proficiency in Python
  • 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
  • Experience designing and delivering production-grade LLM or agent workflows, including tool or function calling, orchestration, and RAG
  • Experience integrating LLM capabilities into services and software development lifecycles, including CI/CD, testing, LLM evaluations, and safe rollout patterns
  • 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
  • Strong technical and non-technical communication skills
  • Ability to collaborate effectively across functions and demonstrate ownership
  • Ability to apply machine learning techniques and emerging AI tools responsibly

Nike Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nike and has not been reviewed or approved by Nike.

  • Retirement Support — A 401(k) with a company match is complemented by options such as a Mega Backdoor Roth and deferred compensation for eligible earners. Financial coaching and structured savings programs support long-term financial security.
  • Equity Value & Accessibility — An Employee Stock Purchase Plan with a stock discount sits alongside broad-based equity vehicles like RSUs and non-qualified stock options. These avenues expand wealth-building opportunities beyond base pay.
  • Parental & Family Support — Paid parental leave includes maternity and paternity time, recently expanded in the U.S. to 16 weeks and extended to part-time retail teammates. Additional supports include childcare assistance at select locations and family-building benefits such as fertility, surrogacy, and adoption.

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The Company
HQ: Beaverton, OR
73,000 Employees
Year Founded: 1972

What We Do

At NIKE, Inc., we innovate to serve athletes*. Every teammate - from coder to creator - plays a role in making these athletes’ dreams real. Our tech, data, and digital teams push limits every day, building the future of sport and the tools that drive it. Here, curiosity is fuel. Innovation is the game plan. Different perspectives keep us on the offense. You’ll solve challenges worth tackling, grow fast and belong to a team that backs you to do the right thing. Bring your drive. Bring your bold. Let’s move the world, together. Take your first step at nike.com/careers * If you have a body, you’re an athlete.

Why Work With Us

At NIKE, Inc., your dedication fuels the future of sport. There is a sense of pride that comes from representing an iconic brand and shaping its future. Here, we treat every day as a new opportunity to push boundaries, ask tough questions and share whole-hearted convictions. We are a team – united by the belief that anything is possible.

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