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1 week ago
As a data quality lead for machine learning engineers, you will be responsible for ensuring the reliability and relevance of training datasets. Your role will involve automating pipelines to guarantee the quality and consistency of data used in large language models.
This is a mission-driven position where you will have the opportunity to shape data pipelines that directly influence next-generation AI systems. If you are passionate about data-centric AI and want to work at the intersection of research and client success, this could be an exciting opportunity for you.
- You will automate pipelines that ensure reliability, safety, and relevance of training datasets.
- You will run proxy fine-tuning and reward modeling experiments to validate quality.
- You will design evaluation frameworks (human + LLM-as-a-judge) to measure consistency and alignment.
- You will collaborate with annotation teams to drive scalable, high-quality data workflows.
- Strong understanding of LLM training & evaluation (SFT, RLHF, reward models).
- Proficiency in Python, PyTorch, Hugging Face, and evaluation tooling.
- Ability to translate model diagnostics into actionable insights.
- Self-starter with a consultative mindset, comfortable working at the intersection of research and client success.
- Work on mission-driven AI projects that push the boundaries of model reliability.
- Shape data pipelines that directly influence next-generation AI systems.
- Join a fast-scaling AI-first team with global impact.
This is an excellent opportunity to join a forward-thinking organization and contribute to the development of cutting-edge AI technology. If you are passionate about data-centric AI and want to make a meaningful impact, we encourage you to apply.
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