Última atualização: 23 de Outubro de 2024
Intermediate Backend Engineer
Via Greenhouse
Sobre
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.
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 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
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
Outras Informações
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