Full-Time

Intermediate Backend Engineer

Modelops:Mlops

Updated on 11/9/2024

GitLab

GitLab

1,001-5,000 employees

Unified DevOps platform for software development

Consumer Software
Enterprise Software

Compensation Overview

$98k - $210kAnnually

Mid

Remote in USA

Category
Backend Engineering
Software Engineering
Required Skills
Kubernetes
Python
Tensorflow
Pytorch
Ruby on Rails
Docker
Web Development
Data Analysis
Requirements
  • 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.
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.

GitLab provides a DevOps platform that simplifies software development by integrating various tools into a single application. This platform enhances collaboration and visibility, allowing teams to focus on improving their products rather than managing multiple tools. GitLab operates on a subscription-based model, offering features for continuous integration and deployment, and serves a diverse range of clients across different industries. The company's goal is to streamline the software development process, making it easier for organizations to develop and deploy software.

Company Stage

IPO

Total Funding

$421.8M

Headquarters

San Francisco, California

Founded

2014

Growth & Insights
Headcount

6 month growth

5%

1 year growth

24%

2 year growth

24%
Simplify Jobs

Simplify's Take

What believers are saying

  • GitLab's potential acquisition by Datadog could significantly enhance its cloud app offerings and market reach.
  • The acquisition of Oxeye for $30-40 million strengthens GitLab's cloud security capabilities, making it a more robust platform for clients.
  • Strategic partnerships, such as with Ooredoo Kuwait and Quokka, demonstrate GitLab's commitment to enhancing its platform's security and efficiency, which can attract more clients.

What critics are saying

  • The potential sale to Datadog introduces uncertainty, which could affect employee morale and client confidence.
  • The competitive DevOps market requires GitLab to continuously innovate to maintain its edge, which can be resource-intensive.

What makes GitLab unique

  • GitLab offers a unified DevOps platform that integrates various tools required for software development, reducing the complexity of managing multiple toolchains, unlike competitors who may offer fragmented solutions.
  • The platform's versatility is demonstrated by its diverse client base, including major corporations across various industries, which is a testament to its broad appeal and adaptability.
  • GitLab's continuous updates and new feature rollouts ensure that clients receive ongoing value from their subscriptions, setting it apart from competitors with less frequent updates.

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Benefits

Spending Company Money

Equity Compensation

Life Insurance

Financial Wellness

Paid Time Off

Growth and Development Benefit

GitLab Contribute

Business Travel Accident Policy

Immigration

Employee Assistance Program

Incentives

All-Remote

Part-time contracts

Meal Train

Fertility & Family Planning

Parental Leave