Senior AI Research Engineer – LLM Training & Agent Systems

Your tasks

You will work on improving model capabilities, reasoning, tool utilization, and task execution, while translating research ideas and emerging AI techniques into reliable, production-ready solutions.

LLM Training and Optimization

• Develop and optimize LLM training and post-training workflows, including supervised fine-tuning (SFT), reinforcement learning (RL), preference optimization, or related techniques.
• Improve model capabilities such as reasoning, instruction following, tool use, and task completion.
• Explore techniques for model adaptation, self-improvement, and training efficiency.

• Design and implement agentic AI systems supporting planning, information retrieval, tool calling, and multi-step task execution.
• Develop reliable workflows for research, automation, and other complex AI applications.
• Explore multi-agent collaboration, asynchronous execution, and agent orchestration where appropriate.

Model Evaluation and Benchmarking

• Build and maintain evaluation frameworks for assessing language models across reasoning, coding, instruction following, and agent performance.
• Develop benchmarking pipelines and analysis tools to measure model quality, reliability, and efficiency.
• Analyze model behavior and use evaluation results to guide further improvements.

Inference and Performance Optimization

• Improve LLM inference performance, resource utilization, and deployment efficiency.
• Work with modern inference and model-serving frameworks such as vLLM, SGLang, and Hugging Face Transformers.
• Explore relevant optimization techniques, including quantization, distributed inference, and GPU acceleration.

Research and Technical Innovation

• Investigate emerging techniques in foundation models, reasoning, reinforcement learning, agent systems, and multimodal AI.
• Prototype and evaluate new approaches, translating promising research into practical applications.
• Collaborate with research, infrastructure, and product teams to deliver AI capabilities into production.
• Contribute to technical documentation, research reports, publications, or open-source projects where appropriate.

Requirements

About the Role We are looking for a Senior AI Research Engineer with experience in Large Language Models (LLMs), model training and optimization, and AI agent systems. The ideal candidate has a strong foundation in machine learning and practical experience in developing, evaluating, or deploying modern AI systems.

• Master's or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent practical experience.
• Strong programming skills in Python and experience with deep learning frameworks such as PyTorch.
• Practical experience in at least one area of LLM development, such as model training, post-training, evaluation, inference optimization, or agent systems.
• Good understanding of transformer architectures, modern foundation models, and key machine learning concepts.
• Experience developing or applying LLM-based systems, evaluation pipelines, or AI applications.
• Familiarity with model deployment, distributed computing, GPU acceleration, or performance optimization.
• Experience building AI applications involving retrieval, tool use, planning, or multi-step workflows is a plus.
• Ability to independently drive technical projects from experimentation and prototyping to implementation and deployment.

Preferred Qualifications

• Experience working with open-source foundation models such as Qwen, Llama, DeepSeek, or similar models.
• Familiarity with LLM training and optimization techniques, including SFT, RLHF, DPO, GRPO, or related approaches.
• Experience with frameworks and tools such as vLLM, SGLang, Hugging Face Transformers, DeepSpeed, or other distributed training and inference frameworks.
• Knowledge of inference optimization techniques, including quantization, mixed-precision computation, and parallelism strategies.
• Experience with AI agents, multi-agent systems, retrieval-augmented generation (RAG), knowledge graphs, or deep research applications.
• Exposure to multimodal AI, search and recommendation systems, model safety, or adversarial robustness.
• Research publications, open-source contributions, or experience applying research techniques to real-world AI systems.