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GitLab

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3.9

based on 7 Reviews

17 GitLab Jobs

Intermediate Machine Learning Engineer, AI Powered: AI Framework

GitLab

3.9

based on 7 Reviews

4-9 years

Remote

1 vacancy

Intermediate Machine Learning Engineer, AI Powered: AI Framework

GitLab

posted 8d ago

Job Role Insights

Flexible timing

Key skills for the job

Python Machine Learning Talent Acquisition Recruitment Open Source Model Validation

+ 2 more

Job Description

An overview of this role

Are you passionate about building robust frameworks to evaluate and ensure the reliability of AI modelsAs a Machine Learning Engineer on GitLab s AI framework team, you ll play a critical role in shaping the future of AI-powered features at GitLab. This is an exciting opportunity to work on impactful projects that directly influence the quality of GitLab s AI capabilities.

You ll help merge cutting-edge evaluation tools, optimize dataset management, and scale our validation infrastructure. Working closely with other AI feature teams, you ll ensure that every AI feature we deliver is robust, reliable, and meets the highest quality standards.

Some challenges in this role include designing scalable solutions for LLM evaluation, consolidating disparate validation tools, and contributing to GitLab s innovative AI roadmap.

Some examples of our projects:

Consolidating Evaluation Tooling | The GitLab Handbook GitLab.org / AI Powered / ELI5

GitLab.org / ModelOps / AI Model Validation and Research / AI Evaluation / Prompt Library

What You ll Do

Design and implement technical evaluators for LLM assessment.
Contribute to evaluation infrastructure consolidation efforts.
Build scalable evaluation pipelines and frameworks.
Develop and manage datasets and evaluation metrics.
Collaborate with feature teams to integrate validation solutions.
Optimize performance across ML evaluation systems.
Support improvements to GitLab s AI-powered tools through validation.
Ensure all solutions align with GitLab s infrastructure and security protocols.

What You ll Bring

Proven experience designing and implementing LLM evaluation systems.
Strong understanding of ML model architectures, including public vs. private implementations.
Expertise in ML evaluation metrics and dataset management.
Demonstrated ability to build production-grade ML infrastructure.
Practical experience with Python-based ML frameworks and evaluation tools (e.g., Langsmith, ELI5).
Excellent problem-solving skills with an engineering mindset.
Ability to collaborate in an asynchronous, remote-first environment.
Familiarity with open-source development and contribution is a plus.

Employment Type: Full Time, Permanent

Read full job description

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