About the job
About Us
At Harmonic, we are pioneering the development of the world's most sophisticated mathematical reasoning engine. Recently, we celebrated our Gold Medal achievement at the 2025 International Math Olympiad (IMO) and are backed by some of the most distinguished investors globally. As we intentionally expand our elite technical team, we invite innovative thinkers to contribute to our vision.
Position Overview
We are looking for a passionate and experienced Lead Research Engineer to join our Reinforcement Learning & Formal Methods team. In this pivotal role, you will spearhead advancements in mathematical theorem proving by leveraging state-of-the-art reinforcement learning techniques. Your expertise will be instrumental in shaping our research vision and executing critical projects that merge reinforcement learning with formal methods to tackle complex theorem proving challenges and more.
Your Key Responsibilities
Lead and conduct groundbreaking research at the intersection of reinforcement learning and formal methods, specifically focusing on mathematical theorem proving.
Innovate and implement new reinforcement learning algorithms and models tailored for theorem proving.
Collaborate with a diverse team of experts to seamlessly integrate reinforcement learning techniques with formal methods.
Stay updated on the latest trends and advancements in reinforcement learning, formal methods, and related domains.
Essential Qualifications
Bachelor's degree in Computer Science, Mathematics, or a related technical field, or equivalent professional experience.
Proven experience in designing and developing innovative reinforcement learning systems.
Proficient programming skills in Python, with solid experience in software development and testing.
Familiarity with deep learning frameworks such as PyTorch.
Strong foundation in mathematical principles, including algebra, geometry, and analysis.
Preferred Qualifications
Master's or PhD in Computer Science, Mathematics, or a related discipline.
Experience applying reinforcement learning to practical challenges within formal methods.
Demonstrated excellence in research through publications, patents, or significant software contributions.
Active involvement in open-source projects or development of software tools pertinent to the field.
