AI Research Engineer specializing in kernel and inference optimization for advanced AI systems at Tether. Driving innovation in model serving architectures with a global remote team.
About the role
- AI Research Engineer innovating post-training methodologies at Tether for agentic behavior and tool use optimization. Engaging in cutting-edge AI research on large language models and decision-making.
Responsibilities
- Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results.
- Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool/function use, multi-agent coordination, and reasoning calibration.
- Design and enhance large-scale post-training systems, including data pipelines, training workflows, evaluation frameworks, and benchmark infrastructure.
- Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment.
- Strengthen feedback loops from real-world product usage, incorporating both explicit and implicit user signals into post-training.
- Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models.
- Closely liaise with research, engineering and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.
Requirements
- Degree in Computer Science, Machine Learning, or a related field; advanced degree (MS/PhD) preferred with a strong publication record in top-tier AI conferences.
- Experience with multimodal post-training workflows and data pipelines, particularly for agentic systems and tool use.
- Hands-on experience applying post-training at scale using distributed training frameworks (e.g., multi-node GPU environments).
- Demonstrated experience improving model capabilities in areas such as reasoning, tool use, and multi-agent coordination that achieve SOTA results.
- Proven track record of open-source contributions related to agentic systems or tool use (code, datasets, or models) on platforms such as GitHub or Hugging Face.
- Publications at leading AI conferences (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, ECCV).
Benefits
- Flexible work arrangements
- Professional development opportunities
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