Intermediate Backend Engineer, GitLab

• Professional experience with Ruby on Rails
• Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, or similar)
• Solid understanding of machine learning concepts and workflows
• Familiarity with containerization (Docker) and orchestration (Kubernetes) technologies
• Experience with Python ML libraries (scikit-learn, TensorFlow, PyTorch) as plus
• Proficiency in the English language, both written and verbal, is sufficient for success in a remote and largely asynchronous work environment
• Demonstrated capacity to clearly and concisely communicate about complex technical, architectural, and/or organizational problems and propose thorough iterative solutions
• Experience with performance and optimization problems and a demonstrated ability to both diagnose and prevent these problems
• Comfort working in a highly agile, intensely iterative software development process
• An inclination towards communication, inclusion, and visibility
• Experience owning a project from concept to production, including proposal, discussion, and execution
• Self-motivated and self-managing, with excellent organizational skills
• Demonstrated ability to work closely with other parts of the organization
• Share our values, and work in accordance with those values
• Ability to thrive in a fully remote organization

Desirable

• As a Backend Engineer on GitLab’s MLOps team, you will be at the forefront of shaping the future of machine learning operations (MLOps) and large language model operations (LLMOps).
• You will play a critical role in enabling GitLab customers to build and integrate their data science workloads directly within GitLab, driving innovation for teams across the globe.
• One of the key challenges you’ll help solve is moving our Experimental and Beta MLOps features to General Availability (GA).
• You’ll work closely with a small, highly collaborative team of engineers, using technologies like Ruby, MLFlow, and GitLab to deliver robust MLOps solutions.
• As part of this team, you will interact with multiple stakeholders across different functions, including teams working on Custom Models, Model Evaluation, and AI Frameworks
• Develop and maintain CI/CD pipelines for ML model deployment in Ruby environments
• Implement and optimize data processing pipelines using Ruby and relevant frameworks
• Collaborate with data scientists to productionize ML models efficiently
• Design and implement monitoring and alerting systems for ML model performance
• Ensure scalability, reliability, and efficiency of ML systems in production
• Contribute to the development of internal MLOps tools and libraries in Ruby
• Develop features and improvements to the GitLab product in a secure, well-tested, and performant way
• Collaborate with Product Management and other stakeholders within Engineering (Frontend, UX, etc.) to maintain a high bar for quality in a fast-paced, iterative environment
• Advocate for improvements to product quality, security, and performance
• Solve technical problems of moderate scope and complexity
• Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale web environment
• Conduct Code Review within our Code Review Guidelines and ensure community contributions receive a swift response
• Recognize impediments to our efficiency as a team (“technical debt”), propose and implement solutions
• Represent GitLab and its values in public communication around specific projects and community contributions
• Confidently ship small features and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projects
• Participate in Tier 2 or Tier 3 weekday and weekend and occasional night on-call rotations to assist in troubleshooting product operations, security operations, and urgent engineering issues