AIResearcher

## Accountabilities Conduct advanced research on agentic AI systems trained on real-world interaction data, focusing on improving reasoning, planning, and tool use. Design and experiment with learning frameworks such as RAG, fine-tuning, RLHF, DPO, and GRPO to enhance large-scale model performance. Develop multimodal representation learning approaches, including joint embedding spaces across text, audio, logs, and structured data. Improve speech and audio intelligence systems, including STT, ASR, and audio-driven learning pipelines. Define evaluation methodologies to measure agent performance in real-world and domain-specific environments. Translate complex behavioral and interaction signals into structured training objectives for large-scale models. Collaborate with engineering and product teams to bring research into production and iterate based on live system feedback. Requirements PhD in Computer Science, Machine Learning, AI, Electrical Engineering, or a related field. 5+ years of experience in applied AI research or ML systems with production-level impact. Strong expertise in large-scale machine learning, LLMs, or multimodal AI systems. Hands-on experience with RAG systems, LLM fine-tuning, and reinforcement learning methods such as RLHF, DPO, or GRPO. Strong background in representation learning, embeddings, and joint multimodal spaces. Experience with speech and audio modeling, including STT, ASR, or audio signal processing. Proficiency in Python and modern ML frameworks such as PyTorch and Hugging Face. Experience designing evaluation frameworks for LLMs or agentic systems. Strong ability to define research hypotheses from ambiguous real-world problems. Excellent written and verbal communication skills in English. Benefits Fully remote position within a global AI research organization Opportunity to shape cutting-edge agentic and multimodal AI systems High-impact research with direct production deployment Collaboration with top-tier engineering and product