Research Engineer, Reinforcement Learning

Techire Ai; San Francisco (CA); On-site; USD 270,000 - 330,000; Full time

Job summary

A technology solutions provider in San Francisco seeks an experienced professional to design and build large-scale reinforcement learning environments. The role involves collaborating with researchers to tackle complex problems, refining learning processes, and scaling infrastructure. Ideal candidates will have extensive machine learning and reinforcement learning experience, a strong intuition for agent dynamics, and a desire to explore challenging scenarios. The compensation package is competitive, with a base salary of up to $300K plus equity.

Qualifications

• Strong ML and RL experience is essential.
• Deep understanding of agent dynamics is required.
• Curiosity and problem-solving skills are needed.

Responsibilities

• Build reinforcement learning environments for training agents.
• Collaborate with researchers on unsolved challenges.
• Scale infrastructure and refine learning processes.

Skills

Machine Learning (ML)

Reinforcement Learning (RL)

Intuition for agent dynamics

Curiosity to explore complex problems

Job description

Want to build the large-scale RL environments frontier labs use to train agents that can truly reason and act?

This team are creating complex reinforcement learning environments — simulations where advanced agents learn to plan, adapt, and solve multi-step problems that stretch beyond standard benchmarks. The focus isn’t on training the models themselves, but on building the worlds that make meaningful learning and evaluation possible — the foundation for more capable, aligned systems.

You’ll work end-to-end across environment design, reward dynamics, and scalable simulation — developing the feedback loops that define what “good” looks like for intelligent behaviour. It’s open-ended, research-driven work where the task definition, data, and reward structure are often the hardest and most important problems to solve.

You’ll collaborate closely with researchers tackling unsolved challenges in reinforcement learning and agent behaviour, shaping experiments, scaling infrastructure, and refining how agents learn in the loop.

It suits someone with strong ML and RL experience, deep intuition for agent dynamics, and the curiosity to explore problems that don’t come with clear instructions.

On-site in San Francisco. Compensation up to $300 K base (negotiable, depending on experience) plus equity.

If you want to help build the environments that teach the next generation of AI systems how to think, act, and adapt — we’d love to hear from you.

All applicants will receive a response.