AI & ML Researchers

Role Overview

ModMed is hiring AI & ML Researchers to drive innovation in large language models (LLMs), AI agents, and advanced machine learning systems. The role sits at the intersection of research and product, translating cutting-edge AI techniques into real-world, measurable impact.

Responsibilities

• Conduct research in LLMs, generative AI, reinforcement learning, and AI agents to advance state-of-the-art capabilities.
• Design and implement algorithms/architectures for LLM fine-tuning, adaptation, and multi-agent collaboration.
• Convert business problems into research-driven AI solutions with stakeholders.
• Build and evaluate models using deep learning, NLP, generative modeling, reinforcement learning, and multi-modal learning.
• Own end-to-end ML lifecycle: exploration & prototyping → production deployment → monitoring.
• Publish research in top-tier conferences and journals.
• Collaborate with product managers, data engineers, and software engineers to ship scalable production systems.
• Partner with data engineering on robust pipelines and infrastructure for training, experimentation, and deployment.
• (Senior/Lead) Mentor junior researchers and data scientists; foster innovation and scientific rigor.

Requirements

• PhD in CS, Electrical Engineering, Mathematics, Physics, or a related quantitative field.
• Strong research record with publications in top ML/AI venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, AAAI, KDD) or journals.
• Experience with machine learning/AI research, including applied LLMs, generative AI, or AI agents.
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
• Hands-on MLOps and cloud experience: AWS, GCP, or Azure.
• Expertise in at least one area:
  • LLMs (pretraining, fine-tuning, prompt engineering, RAG)
  • Autonomous/multi-agent systems
  • Generative AI (text/vision/speech/multimodal)
  • Reinforcement learning & decision-making
• Familiarity with distributed data systems, HPC, and GPU/TPU training.
• Strong communication skills for presenting to both technical and non-technical audiences.

Preferred / Nice-to-have

• Demonstrated ability to translate research into production-grade systems (research-to-product impact).