AI Research Engineer (Agentic Post-training)
New
IndiaFull-Time
Salary not disclosed
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Job Details
- Required Skills
- Machine LearningLLM
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.
Responsibilities
- 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.
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