[Hiring] Machine Learning Ops Engineer II @Sheetz, Inc

Role Description

A Machine Learning Ops Engineer II at Sheetz ensures that AI models move seamlessly from “working on a laptop” to running reliably across our stores, applications, and systems at scale. This role powers capabilities like smarter inventory management, enhanced customer experiences, and faster decision-making that keeps pace with the way Sheetz operates. The MLOps Engineer designs, builds, and maintains the pipelines, deployment processes, and monitoring systems that allow models to run continuously and perform consistently. Just as Sheetz kitchens operate around the clock to serve customers, this role keeps our AI systems running 24/7, using data as the ingredients and algorithms as the recipes that drive our technology.

This role qualifies for a remote work arrangement within our 7 state footprint (PA, OH, MI, WV, VA, MD, NC).

Overview

• Support the design, development, and deployment of ML solutions and infrastructure to operationalize machine learning models and ensure their performance at scale.
• Maintain robust, reproducible, and scalable machine learning workflows.
• Monitor model health in production.
• Assist in implementing MLOps best practices.
• Utilize experience and gain technical depth to contribute to the ongoing maturity of the ML ecosystem across the organization.

Responsibilities (other duties may be assigned)

• Contribute to the design, automation, and maintenance of end-to-end machine learning pipelines, including model training, validation, deployment, and monitoring.
• Write well-structured, testable, and maintainable code to support robust ML systems.
• Apply software engineering best practices to productionize machine learning workflows.
• Collaborate with internal teams to build, integrate, and scale machine learning solutions that align with business and operational requirements.
• Utilize tools including but not limited to MLflow, TensorFlow, PyTorch, and containerization frameworks (e.g., Docker, Kubernetes) to deploy and manage models in production environments.
• Monitor deployed models for drift, latency, and performance degradation; implement alerting and retraining pipelines as needed to maintain reliability, escalating as required.
• Assist in the setup and optimization of CI/CD pipelines for ML workflows to enable fast and safe model iteration and deployment.
• Maintain documentation, version control, and metadata tracking to ensure models are reproducible and auditable.
• Recommend improvements to MLOps practices, frameworks, and tooling and help to define, and refine, operational standards, as the organization’s ML capabilities mature.

Qualifications

• Bachelor’s degree in Computer Science, Management Information Systems, Computer Engineering, or related discipline is required.
• Minimum 3 years experience in design, development, and deployment of ML solutions required.
• Previous utilization of programming languages (Python, Bash) or scripting for automation and ML pipeline orchestration preferred.
• Previous experience in machine learning pipelines, model lifecycle management, or MLOps concepts (e.g., model deployment, monitoring, CI/CD) preferred.
• Previous experience in secure development practices and cloud environments (e.g., AWS, GCP, or Azure) preferred.
• Certifications in cloud platforms (AWS/GCP/Azure), ML Ops, or DevOps tools preferred.

Benefits

• Base salary range of $78,807.00 - $131,346.00 per year, depending on experience and qualifications, plus bonus based on company performance.
• Quarterly employee bonuses based on company performance.
• Competitive salaries.
• PTO and parental leave.
• 401k match and employee stock ownership.
• Limitless professional development and growth opportunities.
• Tuition reimbursement.
• Full medical, vision, and dental coverage.
• Snack discounts.

Accommodations

Sheetz is committed to the full inclusion of all qualified individuals. Sheetz is committed to considering all applicants regardless of disability who can perform all essential job duties with or without accommodations.