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An overview of this role

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.

The team currently includes two Staff Fullstack Engineers and is set to grow by adding two more Backend Engineers. This expansion allows you to impact the product and the larger GitLab community directly, ensuring our MLOps features meet the highest standards and serve a wide range of users. Whether you're located in AMER, EMEA, or APAC, this remote-first team offers the flexibility to collaborate globally while having a significant voice in the direction of MLOps at GitLab.

Success in this role means delivering against your assigned work, contributing to the team’s goals, and helping GitLab push the boundaries of MLOps and LLMOps. With growth plans on the horizon, this is a great opportunity to be part of a pioneering team at the cutting edge of machine learning.

To dive deeper into the team's work and roadmap, check out our handbook and Group Direction.

What You’ll Do  

• 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 Reviewwithin 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

What You’ll Bring 

• 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.

How To Stand Out

• Have contributed a merge request to GitLab or an open source project in the ML space
• A Masters or PhD in Data Science or similar discipline
• Professional Python or Golang experience

About the team

The MLOps team at GitLab is on a mission to empower users to seamlessly integrate and manage their data science workloads within the GitLab platform. Our goal is to make machine learning operations (MLOps) and large language model operations (LLMOps) more accessible, ensuring that teams can build, train, evaluate, and deploy their models directly from GitLab. By integrating these complex workflows, we help teams enhance productivity, streamline model deployment, and ensure continuous integration and delivery for machine learning models.