Job Description
Join Nexus AI Labs as a Senior Agentic AI Researcher and help shape the trajectory of autonomous intelligence. As we look toward the technological horizon of 2026, we are building the next generation of AI systems capable of complex, multi-step reasoning and self-improvement.
In this pivotal role, you will lead the research and development of Agentic Workflows, ensuring our AI agents are safe, efficient, and capable of solving real-world problems with minimal human intervention. You will work at the intersection of large language models (LLMs), reinforcement learning, and AI safety protocols.
Why Join Us?
- Work on cutting-edge projects that define the future of AI.
- Competitive compensation package with equity options.
- Flexible remote-first policy with a hub in San Francisco.
Responsibilities
- Architect Agentic Systems: Design and implement autonomous agents capable of planning, executing, and learning from multi-step tasks in dynamic environments.
- AI Safety & Alignment: Develop robust guardrails and alignment techniques to ensure AI behaviors remain ethical, unbiased, and controllable.
- Model Optimization: Fine-tune and optimize state-of-the-art transformer architectures for higher reasoning accuracy and reduced latency.
- Research Publication: Publish high-impact research papers and contribute to open-source communities to advance the field of AI.
- Collaboration: Partner with cross-functional teams including engineering, product management, and ethics boards to translate research into deployable products.
- Experimental Design: Conduct rigorous A/B testing and benchmarking to measure agent performance against evolving 2026 standards.
Qualifications
- Education: Ph.D. in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
- Technical Mastery: Deep expertise in Python, PyTorch, or JAX, with a proven track record of implementing complex algorithms from scratch.
- LLM Experience: Strong background in Large Language Models, including fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
- Reinforcement Learning: Hands-on experience with Reinforcement Learning from Human Feedback (RLHF) and model-based RL.
- Problem Solving: Demonstrated ability to tackle ambiguous, open-ended problems and drive innovation in uncharted territory.
- Communication: Exceptional written and verbal communication skills, with the ability to explain complex technical concepts to diverse audiences.