Senior ML Research Engineer driving the research and development of multimodal embedding models at TwelveLabs. Collaborating on projects integrating video, audio, and text for innovative AI solutions.
Responsibilities
Design and execute experiments to improve multimodal embedding model quality, spanning model architecture, training methodology, data composition, and evaluation
Build and optimize large-scale distributed training pipelines (multi-node, multi-GPU) for contrastive and representation learning
Develop and improve data curation, filtering, and quality assessment pipelines at scale
Conduct ablation studies to systematically evaluate design choices and communicate findings to guide technical direction
Implement evaluation frameworks and benchmarks that rigorously measure embedding model quality
Collaborate with the search/serving team to ensure model improvements translate to end-to-end retrieval quality gains
Requirements
4–7 years of industry experience in computer vision, NLP, or multimodal learning, with a track record of shipping ML systems to production
Strong proficiency in Python and PyTorch, with hands-on experience in distributed model training
Experience in contrastive learning, representation learning, or embedding models, demonstrated through shipped products, publications, or open-source contributions
End-to-end ownership experience: taking a model from research idea through training to production deployment, not just running experiments in isolation
Ability to independently drive research projects from problem definition through experiment design to conclusions
Effective communication skills for collaborating with colleagues from diverse backgrounds
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