Senior MLOps Engineer

EquipmentShare is Hiring a Senior MLOps Engineer.

EquipmentShare is searching for a Senior MLOps Engineer for a remote opportunity supporting the Data Platform team as it continues to grow.  

Primary Responsibilities

• Collaborate with scientists and data engineers to design, develop, and deploy scalable ML models and pipelines
• Work with scientists to build and maintain end-to-end ML pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
• Automate continuous integration and continuous deployment (CI/CD) pipelines for scientists to self-service model and feature deployment
• Develop monitoring and logging solutions to track model performance, data quality, and system health in production environments
• Work with Scientists to optimize ML workflows for efficiency, scalability, and cost-effectiveness.
• Automate infrastructure provisioning, configuration management, and resource scaling using infrastructure-as-code (IaC) tools
• Troubleshoot issues related to data, models, infrastructure, and deployments in production environments
• Mentor junior members of the Data Platform team and provide technical guidance and best practices
• Stay updated on the latest advancements and trends in MLOps, machine learning, and cloud technologies

Why We’re a Better Place to Work

• Competitive salary.
• Medical, Dental and Vision coverage for full-time employees.
• 401(k) and company match.
• Unlimited paid time off (PTO) plus company paid holidays.
• Generous paid parental leave
• State of the art onsite gym (Corporate HQ) with instructor led-courses/Gym stipend for remote employees.
• Seasonal and year round wellness challenges.
• Company sponsored events (annual family gatherings, happy hours and more).
• Volunteering and local charity initiatives that help you nurture and grow the communities you call home. Employees receive 16 hours of paid volunteer time per year. 
• Opportunities for career and professional development with conferences, events, seminars and continued education. 

About You 

Our mission to change an entire industry is not easily achieved, so we only hire people who are inspired by the goal and up for the challenge. In turn, our employees have every opportunity to grow with us, achieve personal and professional success and enjoy making a tangible difference in an industry that’s long been resistant to change. 

Skills & Qualifications 

• Bachelor’s or Master’s degree, or equivalent practical experience, in computer science, machine learning, software engineering, data engineering, DevOps, or related field
• 5+ years of experience working in MLOps, machine learning engineering, software engineering, data engineering, DevOps, or related roles
• Demonstrated knowledge and experience in building a modern data science, experimentation and machine learning technology stack for a data-driven company
• Proficiency in programming languages such as Python, Java, or Scala
• Strong understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes)
• Knowledge of DevOps practices, including infrastructure automation, configuration management, and monitoring
• Excellent problem-solving skills and attention to detail
• Effective communication and collaboration skills, with the ability to work in a fast-paced, team-oriented environment
• Experience with version control systems (e.g., Git) and CI/CD tools (e.g., Jenkins, GitLab CI/CD)
• Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with Snowflake is preferred but not required
• Must be qualified to work in the United States or the United Kingdom - we are not sponsoring any candidates at this time

EquipmentShare is committed to a diverse and inclusive workplace. EquipmentShare is an equal opportunity
employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation,
protected veteran status, disability, age, or other legally protected status.

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