About the role
Mirendil Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We believe accelerating scientific discovery is one of the most powerful ways to improve the future of humanity, and that AI will play a central role in making that possible. We are building a frontier AI research company and training our own models end-to-end. Our work spans areas such as model training, reinforcement learning, reasoning systems, and infrastructure for large-scale experiments. Our team includes researchers and engineers from Anthropic, Google DeepMind, xAI, OpenAI, Microsoft, Apple, and MIT.
The Role We are looking for an engineer who is passionate about giving the model tools to perform the best it can. We want people who deeply understand model capabilities and can build efficient architectures for the model and researchers to work across. If you have a penchant for building your own tools, this role will be a good fit. Some example areas you might work on: Build and innovate on the agent harness: agent loop architecture, tool integrations, prompt scaffolding, execution environments, and capability primitives Design orchestration systems for horizontal scaling of agents: memory, state management, multi-agent coordination, and task decomposition Build guardrails and reliability mechanisms that make long-horizon agentic tasks robust across failures, unexpected model behavior, and edge cases Own the extension layer between our models and external tools, APIs, and environments - making it fast to bring new capabilities online Develop evaluation and observability tooling so
the team can measure agent behavior, catch regressions, and iterate quickly If you're excited about building the infrastructure that makes agents actually work at scale, we'd love to hear from you. We offer a base salary of $350,000–$500,000 USD and a meaningful equity grant, depending on experience and background, along with competitive
