Lead Machine Learning Engineer – AI/ML (Remote Work Option)

Posted:
10/8/2024, 5:00:00 PM

Location(s):
Beaverton, Oregon, United States ⋅ Oregon, United States

Experience Level(s):
Senior

Field(s):
AI & Machine Learning

Workplace Type:
Remote

Open to remote work except in South Dakota, Vermont and West Virginia.

The annual base salary for this position ranges from $119,400.00 in our lowest geographic market to $267,500.00 in our highest geographic market. Actual salary will vary based on a candidate's location, qualifications, skills and experience.

Information about benefits can be found here.
 

WHO WE ARE LOOKING FOR

Nike is looking for a Lead Machine Learning Engineer to join our growing team. This role is part of a squad within Enterprise Data & AI AI/ML team, that is responsible for building solutions that will transform marketing experiences and workflows at Nike. Playing a key role in the development of digital tools, processes, and experiences, the ideal candidate is a problem-solver who is motivated to learn new technologies, communicate ideas and knowledge, and collaborate with teammates. You will work with a cross-functional team to build solutions. You need to be able to work with ambiguity and abstract requirements, developing features quickly in a team context, balancing speed with quality. Above all, your work will accelerate Nike's core mission of serving Athletes*

WHO WILL YOU WORK WITH?

Our team has Machine Learning Engineers, Software Engineers, and Data Scientists. You will work day-to-day with your peer Machine Learning Engineers, Software Engineers, Product Management and other squad team members to achieve business objectives. You will also engage with other Global Technology functions and teams on organizational and technical goals.

WHAT YOU WILL DO

  • Apply machine learning, specifically computer vision and generative AI techniques to solve business problems
  • Provide technical leadership, establishing standards and principles for your team to follow.
  • Support investigation of new software packages/tools, APIs, and algorithms to deliver quality analytics and machine learning at scale
  • Collaborate with a cross functional agile team of software engineers, data engineers, ML experts, and others to build new product features
  • Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring
  • Build Front-end solutions to enable end users to interact with served models 
  • Write production-quality code for ML models as online services and APIs
  • Present complex analyses clearly and concisely
  • Ability to build collaborative relationships with peers and multi-functional partners
  • Provide support by writing documentation and tutorials as well as providing guidance to users with a variety of technical skills.

WHAT YOU BRING

  • Bachelor Degree or a combination of relevant education, training and experience
  • Advanced degrees a plus (PhD, Masters, etc.)
  • 5+ plus years of experience in enterprise environment with a combination of technology and team leadership responsibilities.
  • Expertise with Python, Spark, or Java.
  • Expertise in NodeJS, React, or Vue.js a plus 
  • This role requires strong leadership skills to mentor and guide a team of Machine Learning Engineers. Must have a balance of technical expertise, strategic planning, and team management to ensure project execution and workforce optimization.  
  • Expertise in building and productionalizing large scale consumer facing ML models
  • Designed, built and shipped applications that scale and implementing best practices in ML Ops and CI/CD to build state of the art ML models
  • Proficient at writing good quality, well-documented and tested, scalable code - Python preferred. Experience with tools like mlFlow, Airflow, Docker and Cloud Platforms such as AWS/GCP is ideal. Experience deploying, monitoring and maintaining data science products in cloud environments such as AWS
  • Knowledge of techniques for model compression, quantization, and optimization for deployment in resource-constrained environments.
  • Experience with data processing and storage frameworks like S3, Spark, Dynamo, etc.