123 applicants

GitLab · 1 day ago

Intermediate Backend Engineer, ModelOps:MLOps

United States

Full-time

Remote

Mid Level

$98K/yr - $210K/yr

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Comp. & Benefits

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Responsibilities

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

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Ruby on RailsMLOps practicesMachine learning conceptsDockerKubernetesPython ML librariesMLflowKubeflowScikit-learnTensorFlowPyTorchEnglish language proficiencyAgile development

Required

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

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.