Tbwa Chiat/Day Inc
Intermediate Backend Engineer, AI Powered: ModelOps at GitLab
Tbwa Chiat/Day Inc, Georgia Center, Vermont, United States,
RemoteGitLab is an open core software company that develops the most comprehensive AI-powered DevSecOps Platform, used by more than 100,000 organizations. Our mission is to enable everyone to contribute to and co-create the software that powers our world. When everyone can contribute, consumers become contributors, significantly accelerating the rate of human progress. This mission is integral to our culture, influencing how we hire, build products, and lead our industry. We make this possible at GitLab by running our operations on our product and staying aligned with our values.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.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.Responsibilities
Develop and maintain CI/CD pipelines for ML model deployment in Ruby environmentsImplement and optimize data processing pipelines using Ruby and relevant frameworksCollaborate with data scientists to productionize ML models efficientlyDesign and implement monitoring and alerting systems for ML model performanceEnsure scalability, reliability, and efficiency of ML systems in productionContribute to the development of internal MLOps tools and libraries in RubyDevelop features and improvements to the GitLab product in a secure, well-tested, and performant wayCollaborate with Product Management and other stakeholders within Engineering (Frontend, UX, etc.) to maintain a high bar for quality in a fast-paced, iterative environmentAdvocate for improvements to product quality, security, and performanceSolve technical problems of moderate scope and complexityCraft code that meets our internal standards for style, maintainability, and best practices for a high-scale web environmentRecognize impediments to our efficiency as a team (“technical debt”), propose and implement solutionsRepresent GitLab and its values in public communication around specific projects and community contributionsConfidently ship small features and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projectsParticipate 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 issuesWhat You’ll Bring
Professional experience with Ruby on RailsExperience with MLOps practices and tools (e.g., MLflow, Kubeflow, or similar)Solid understanding of machine learning concepts and workflowsFamiliarity with containerization (Docker) and orchestration (Kubernetes) technologiesExperience with Python ML libraries (scikit-learn, TensorFlow, PyTorch) as plusProficiency 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 spaceA Masters or PhD in Data Science or similar disciplineProfessional Python or Golang experienceAbout 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.Our team is still growing, and we’re set to expand by adding Backend Engineers to help scale these efforts. We work closely with other teams, such as Custom Models, Model Evaluation, and AI Frameworks, to deliver features that support a wide range of machine learning use cases.Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.Salary Information:$98,000 - $210,000 USDCountry Hiring Guidelines:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics.Apply for this job
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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.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.Responsibilities
Develop and maintain CI/CD pipelines for ML model deployment in Ruby environmentsImplement and optimize data processing pipelines using Ruby and relevant frameworksCollaborate with data scientists to productionize ML models efficientlyDesign and implement monitoring and alerting systems for ML model performanceEnsure scalability, reliability, and efficiency of ML systems in productionContribute to the development of internal MLOps tools and libraries in RubyDevelop features and improvements to the GitLab product in a secure, well-tested, and performant wayCollaborate with Product Management and other stakeholders within Engineering (Frontend, UX, etc.) to maintain a high bar for quality in a fast-paced, iterative environmentAdvocate for improvements to product quality, security, and performanceSolve technical problems of moderate scope and complexityCraft code that meets our internal standards for style, maintainability, and best practices for a high-scale web environmentRecognize impediments to our efficiency as a team (“technical debt”), propose and implement solutionsRepresent GitLab and its values in public communication around specific projects and community contributionsConfidently ship small features and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projectsParticipate 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 issuesWhat You’ll Bring
Professional experience with Ruby on RailsExperience with MLOps practices and tools (e.g., MLflow, Kubeflow, or similar)Solid understanding of machine learning concepts and workflowsFamiliarity with containerization (Docker) and orchestration (Kubernetes) technologiesExperience with Python ML libraries (scikit-learn, TensorFlow, PyTorch) as plusProficiency 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 spaceA Masters or PhD in Data Science or similar disciplineProfessional Python or Golang experienceAbout 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.Our team is still growing, and we’re set to expand by adding Backend Engineers to help scale these efforts. We work closely with other teams, such as Custom Models, Model Evaluation, and AI Frameworks, to deliver features that support a wide range of machine learning use cases.Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.Salary Information:$98,000 - $210,000 USDCountry Hiring Guidelines:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics.Apply for this job
* indicates a required field
#J-18808-Ljbffr