About the role
This is a full-time, in-person role based in Los Altos, California. This position requires working on site.
We are looking for an exceptional Applied Researcher / Research Engineer to join our team and build frontier AI systems. We are building frontier Recursive Self-Improvement (RSI) systems that enable AI agents to continuously learn and improve in real, complex environments, and apply those capabilities to enterprise-scale problems.
You will have access to large-scale GPU compute, real enterprise environments, and high-value proprietary data and complex tasks from leading companies around the world. You will directly contribute to the training, experimentation, development, and iteration of next-generation AI systems.
What you’ll do
- Build and iterate on frontier AI agents, enabling systems to continuously improve on complex, real-world tasks.
- Contribute to Recursive Self-Improvement, agent learning, long-horizon reasoning, and LLM post-training.
- Rapidly turn technical ideas into working systems that can be tested and evaluated.
- Build advanced agent systems spanning reasoning, tool use, memory, context management, and multi-step workflows.
- Use large-scale GPU compute for training, experimentation, and rapid iteration.
- Contribute to post-training through reinforcement learning, supervised fine-tuning (SFT), distillation, and related methods.
- Build benchmarks, evaluation pipelines, and automated graders.
- Analyze agent trajectories and failure modes to identify key bottlenecks in models, data, and systems.
- Build and process high-quality training data, including real interaction data and synthetic data.
- Work with Founding Members and customer teams to rapidly translate defined problems into experiments, prototypes, and production systems.
- Continuously improve agent accuracy, reliability, latency, token efficiency, and end-to-end task completion.
What we’re looking for
- A bachelor’s, master’s, or PhD degree in Computer Science, AI, Machine Learning, or a related field.
- A solid foundation in machine learning and deep learning.
- Strong hands-on engineering and implementation skills, with the ability to quickly turn a technical idea into a working system.
- Project experience with LLMs, AI agents, reinforcement learning, post-training, evaluation, or related areas.
- Proficiency in Python and familiarity with the mainstream deep learning and LLM ecosystem.
- The ability to quickly understand the latest AI research and implement and validate its key methods.
- The ability to independently debug systems, analyze failure modes, and improve performance through experimentation.
- Strong execution, learning speed, and ownership.
- The ability to communicate effectively with customers and teammates as needed, while keeping building and execution at the center of the role.
Nice to have
- Hands-on experience developing production LLM or agent systems.
- Experience with reinforcement learning, SFT, distillation, or other post-training methods.
- Familiarity with PyTorch, vLLM, SGLang, Ray, or related training and inference infrastructure.
- Experience with long-horizon agents, coding agents, or tool-use systems.
- Experience with benchmarks, LLM-as-a-judge, reward models, or evaluation systems.
- Experience with synthetic data generation or large-scale data pipelines.
- Publications, open-source projects, competitions, or other evidence of technical ability.
- Experience at a startup or on a research or engineering team that iterates rapidly.
Apply
To apply, email info@evolocity.ai with the role title in the subject line.