AI Research Engineer (Agentic Post-training)

Overview

Accountabilities

• Conduct end-to-end research and engineering work to advance post-training methods for agentic AI systems, focusing on tool use, reasoning, and autonomous behavior in real-world tasks.
• Improve core model capabilities including factuality, instruction following, multi-step reasoning, tool/function calling, and multi-agent coordination.
• Design, build, and optimize large-scale post-training pipelines, including data curation workflows, training infrastructure, and evaluation frameworks.
• Develop robust benchmarking and diagnostic systems to assess model performance, reliability, and readiness for deployment.
• Integrate real-world feedback signals from production usage into training loops to continuously enhance model behavior.
• Collaborate closely with research, engineering, and product teams to ensure scalable, production-ready integration of agentic capabilities.
• Identify bottlenecks in current systems and propose novel solutions to improve efficiency, reliability, and performance of tool-augmented models.

Requirements

• Degree in Computer Science, Machine Learning, or a related field; advanced degree (MS/PhD) strongly preferred.
• Strong background in large language models, with proven experience in post-training techniques such as fine-tuning, reinforcement learning, or instruction tuning.
• Hands-on experience with distributed training systems and large-scale model development (e.g., multi-GPU or multi-node environments).
• Demonstrated expertise in improving model reasoning, tool use, function calling, or agentic workflows to achieve state-of-the-art performance.
• Experience working with multimodal data (text, image, audio) and building or optimizing data pipelines for AI training.
• Strong track record of research contributions, ideally including publications at top-tier AI conferences (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, ECCV).
• Open-source contributions related to AI agents, tool use, or LLM systems (e.g., GitHub, Hugging Face) is highly valued.
• Strong analytical thinking, problem-solving skills, and ability to work in fast-paced, research-intensive environments.
• Excellent communication skills and ability to collaborate effectively across technical and cross-functional teams.

Benefits

• Remote-first and globally distributed working environment.
• Opportunity to work on cutting-edge AI systems shaping the future of agentic intelligence.
• Exposure to large-scale, real-world AI deployments and advanced research problems.
• Collaborative environment with top-tier researchers and engineers across AI, systems, and product domains.
• High-impact role with strong ownership over research and engineering initiatives.
• Continuous learning and professional growth in a fast-evolving deep-tech ecosystem.
• Competitive compensation and performance-based growth opportunities (where applicable).

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