Design and implement optimization algorithms (RL, genetic algorithms, search-based methods) for agent systems Work directly with enterprise customers to understand their agent architectures and pain points Build evaluation frameworks and benchmarks to measure agent performance improvements Collaborate with the product team to translate research insights into scalable product features Publish technical blog posts and contribute to the broader AI community Help define the technical roadmap for Lucidic's optimization platform
What We Look For
Strong background in machine learning, reinforcement learning, or optimization Experience building and deploying ML systems in production Proficiency in Python and modern ML frameworks (PyTorch, JAX, etc.) Excellent communication skills and ability to work directly with customers Self-motivated with ability to thrive in an early-stage startup environment
Nice to Have
Experience with LLM prompt optimization or agent systems Publications in top ML venues (NeurIPS, ICML, ICLR, etc.) Prior startup experience or entrepreneurial background Experience with distributed systems and cloud infrastructure
About Lucidic AI
We build tooling that automatically evaluates and improves enterprise AI agents using search + learning loops across prompts, tools, and policies. Our customers care about reliability, auditability, and measurable gains. Apply for this role