Remote AI Agentic Engineer & Tooling Optimizer

Yo Ai Labs; Neuquén; Remote; ARS 182,771,000 - 289,387,000; Full time

Job summary

Tether seeks an AI Research Engineer (Agentic Post-training) for a 100% remote worldwide role. You will advance post-training methods to enable agentic behavior, tool usage, and autonomous tool invocation on edge devices, spanning mobile to distributed systems.

You will work on multimodal, scalable post-training pipelines, improving reasoning and decision-making with reinforcement learning from feedback and global collaboration with cross-functional teams.

Qualifications

• Degree in Computer Science, Machine Learning, or a related field; advanced degree (MS/PhD) preferred.
• Experience with multimodal post-training workflows and data pipelines for agentic systems.

Responsibilities

• Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models.
• Drive cross-cutting model improvements including factuality, instruction adherence, tool use, and multi-agent coordination.
• 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 into post-training.

Skills

Large language models

Agentic workflows

Tool use

Function calling

Reinforcement learning from feedback

Multimodal AI

Distributed training frameworks

Open-source contributions

Publications in AI conferences

Education

MS/PhD in CS/ML or related field

Tools

Distributed training environments

GitHub

Hugging Face

Job description

Tether seeks an AI Research Engineer (Agentic Post-training) for a 100% remote worldwide role. You will advance post-training methods to enable agentic behavior, tool usage, and autonomous tool invocation on edge devices, spanning mobile to distributed systems.

You will work on multimodal, scalable post-training pipelines, improving reasoning and decision-making with reinforcement learning from feedback and global collaboration with cross-functional teams.