Engineer, Machine Learning
Pilot Flying J is the 10th largest privately held company in North America with more than 28,000 team members. As the industry-leading network of travel centers, we have more than 950 retail and fueling locations in 44 states and six Canadian provinces. Our energy and logistics division is a top supplier of fuel, employing one of the largest tanker fleets and providing critical services to oil operations in our nation's busiest basins. Pilot Company supports a growing portfolio of brands with expertise in supply chain and retail operations, logistics and transportation, technology and digital innovation, construction, maintenance, human resources, finance, sales and marketing.
Founded in 1958, we are proud to be family owned and consider our team members to be part of the family. Our founding values, people-first culture and commitment to giving back remains true to us today. Whether we are serving guests, a fellow team member, or a trucking company, we are dedicated fueling people and keeping North America moving.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
Pilot Flying J is part of the Pilot Company family of brands that keeps North America's drivers moving, including E-Z Trip, Mr. Fuel, One9 Fuel Stop, Pride, StaMart and Xpress Fuel.
As a Machine Learning Engineer, you will be an instrumental member of our Analytics Center of Excellence. You will implement machine learning models into production by utilizing state-of-the-art tools/algorithms and methodologies following DevOps and a test-driven development process. You will work in close collaboration with the data scientists and guide them to focus not only on model performance but also delivery stability, reproducibility, and scalability of a software product.
- Assist in continuous monitoring of production pipelines to detect and diagnose failures in the machine learning models
- Develop and implement best practices for ML Ops by working in close collaboration with data engineering, data science and DevOps.
- Perform root cause analysis on failures and implement measures to minimize production failures.
- Optimize the machine learning code for performance to ensure the models continue to meet predefined SLAs.
- Deliver systematic approaches, integrating work into applications and tools with our influence, build and maintain the large-scale analytics infrastructure required for the AI projects, and integrate with external IT infrastucture/service to provide e2e solutions.
- Leverage an understanding of software architecture and software patterns to write scalable, maintainable, well-designed and future-proof code.
- Design, develop, and maintain the framework for analytical pipeline.
- Design machine learning systems and implement appropriate ML algorithms and tools in collaboration with the data science team.
- Advocate and educate on the value of data directed outcomes making focusing on the "how and why’ of problem solving.
- Model behaviors that support the company’s common purpose; ensure guests and team members are supported at the highest level
- Ensure all activities are in compliance with rules, regulations, policies, and procedures
- Complete other duties as assigned
- Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, or related field required
- Master’s degree preferred.
- Minimum 5 years experience in a quantitative discipline, data science, Machine Learning, Software Engineering, or Python frameworks required
- Minimum 2 years experience in cloud platforms such as AWS, Azure, or GCP for deploying machine learning models required
- Proficiency in programming languages Java, C++, Python, or R
- Ability to automate the development process of a machine learning project
- Proficiency with tools such as container, continuous integration and delivery, and orchestration tools.
- Experience with ML algorithms for classification, regression, and time series processes
All your information will be kept confidential according to EEO guidelines.