Remote Agentic AI Post-Training Engineer

Alignerr Corp.; Neuquén; Remote; ARS 185,448,000 - 247,265,000; Full time

Job summary

Tether is hiring an AI Research Engineer (Agentic Post-training) to advance agentic and tool-augmented models with state-of-the-art post-training techniques. You will work on end-to-end research, data pipelines, and evaluation frameworks to enable reliable, goal-directed AI systems that can operate across edge devices and cloud environments.

The role emphasizes multimodal inputs, tool invocation, and reinforcement learning from feedback, with an emphasis on scalable, reproducible research and

Qualifications

• 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.

Responsibilities

• Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results.
• Drive model improvements in factuality, instruction adherence, tool 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 with explicit and implicit signals.
• Collaborate with tooling, product, and training teams to improve usefulness and reliability of frontier models.
• Liaise with research, engineering and cross-functional teams to determine production-ready integrations.

Skills

LLM architectures

Post-training

Multimodal

Distributed training

Research

Education

BSc / MSc in CS or ML

Tools

Tool use APIs

RLHF

Job description

Tether is hiring an AI Research Engineer (Agentic Post-training) to advance agentic and tool-augmented models with state-of-the-art post-training techniques. You will work on end-to-end research, data pipelines, and evaluation frameworks to enable reliable, goal-directed AI systems that can operate across edge devices and cloud environments.

The role emphasizes multimodal inputs, tool invocation, and reinforcement learning from feedback, with an emphasis on scalable, reproducible research and