Job Description
We are building the operating system for autonomous intelligence, and we are looking for a visionary Next-Gen Agentic AI Engineer to join our elite team in San Francisco. As we look toward the future of 2026, we are moving beyond static models to create self-sustaining, goal-oriented AI agents capable of complex reasoning and multi-step execution.
If you are passionate about the intersection of LLMs, Reinforcement Learning, and Cognitive Architecture, this is your opportunity to define the standard for human-AI collaboration.
Why Join Us?
- Work on projects that define the roadmap for 2026 and beyond.
- Competitive equity package and top-tier compensation.
- Access to cutting-edge hardware for model training.
Responsibilities
- Design and implement the architecture for autonomous agentic systems capable of planning, executing, and self-correcting complex tasks.
- Optimize large language models for low-latency, high-throughput inference in production environments.
- Collaborate with cognitive scientists to refine agent reasoning loops and memory architectures.
- Build and maintain robust evaluation frameworks to measure agent performance against human benchmarks.
- Drive the integration of multimodal inputs (text, vision, audio) into unified agent workflows.
- Contribute to the open-source ecosystem, sharing best practices for scalable AI infrastructure.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
- 5+ years of experience in machine learning engineering, specifically with PyTorch or TensorFlow.
- Deep understanding of Large Language Models (LLMs), RAG pipelines, and fine-tuning methodologies.
- Experience with distributed computing systems (Kubernetes, Docker, Ray) and cloud infrastructure (AWS/GCP).
- Strong proficiency in programming languages including Python, C++, and Rust.
- Proven track record of shipping complex AI products to market.