AI Research Engineer (Agentic Post-training) - 100% Remote Worldwide

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Join the AI model team to advance post-training for agentic, tool-using large language models. You will design and optimize post-training workflows, curate tool-use data, and evaluate multi-modal systems to enable goal-directed behavior and reliable tool invocation across edge devices, collaborating with cross-functional teams in a fully remote, global setup.

Location: Italy, Argentina, Colombia, Brazil, Uruguay, India, Bangladesh, Pakistan, Vietnam, Taiwan, Thailand, United Kingdom, Switzerland, Spain, United Arab Emirates, Bulgaria, Czech Republic, Serbia, Denmark, Estonia, Greece, Georgia, Budapest, Ireland, Malta, Norway, Amsterdam, Poland, Portugal, Romania, Sweden, Belgium, Israel, Cyprus

Workplace: Remote

Employment Type: Full time

Job Function: Education & Training

Key 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.

Key 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.

Education:Bachelor's

Skills:MultimodalDistributed trainingTool useReinforcement learning from feedbackData pipelinesLLMsPost-trainingAgenticEvaluation frameworks

Languages:English

Tech Stack:PythonPyTorchGPUDistributed trainingMultimodalTool useFunction callingReinforcement learning

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website

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Tether.io

2 months ago

AI Research Engineer (Agentic Post-training) - 100% Remote Worldwide

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Workplace: Remote

Employment Type: Full time

Job Function: Education & Training

Key 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.

Key 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.

Education:Bachelor's

Skills:MultimodalDistributed trainingTool useReinforcement learning from feedbackData pipelinesLLMsPost-trainingAgenticEvaluation frameworks

Languages:English

Tech Stack:PythonPyTorchGPUDistributed trainingMultimodalTool useFunction callingReinforcement learning

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website