Member of Technical Staff, Machine Learning

Sieve is a multimodal lab curating high-quality training datasets across video, audio, images, text, and 3D. The Machine Learning Engineer will own the ML lifecycle, including dataset design, model improvement, evaluation systems, and production pipelines that improve dataset quality. The role works directly with frontier AI labs to build scalable solutions for complex multimodal data problems.

Responsibilities:

• Own model quality for customer-facing video understanding problems
• Fine-tune vision-language and multimodal foundation models for specialized tasks
• Build automated evaluation and QA pipelines using frontier models like Gemini, GPT, Claude, and open-source VLMs
• Design high-precision filtering, ranking, retrieval, and labeling systems over internet-scale video datasets
• Create datasets, benchmarks, and evaluation frameworks that continuously improve model quality
• Develop production ML pipelines spanning preprocessing, inference, post-processing, and quality validation
• Work directly with frontier AI labs to translate ambiguous requirements into scalable ML systems
• Ship improvements quickly, measure results, and iterate based on real-world performance