Research Engineer, Reinforcement Learning

Harmonic; Palo Alto (CA); On-site; USD 90,000 - 150,000; Full time

Job summary

An innovative startup is seeking a passionate Research Engineer to push the boundaries of mathematical theorem proving through advanced reinforcement learning techniques. This role offers the opportunity to lead groundbreaking research and develop cutting-edge algorithms that integrate RL with formal methods. Join a dynamic team dedicated to solving complex problems and advancing the field of mathematical reasoning. If you're excited about the intersection of AI and mathematics, this is your chance to make a significant impact in a fast-paced environment.

Qualifications

• BS in a technical field or equivalent experience required.
• Strong programming skills in Python and experience in RL systems.

Responsibilities

• Lead research in RL and formal methods for theorem proving.
• Develop novel RL algorithms and collaborate with a multidisciplinary team.

Skills

Reinforcement Learning

Python Programming

Mathematical Concepts

Deep Learning

Education

Bachelor's in Computer Science

Master's or PhD in Computer Science

Tools

PyTorch

Job description

Harmonic is a startup building the world’s most advanced mathematical reasoning engine. Backed by some of the world's most prominent investors, we are intentionally scaling our elite technical team.

We are seeking a highly motivated and experienced Research Engineer to join our Reinforcement Learning & Formal Methods team. The focus of this position will be on leading advancements in mathematical theorem proving using cutting-edge RL techniques. The successful candidate will play a key role in developing new algorithms and models that integrate RL with formal methods to solve complex problems in theorem proving and beyond.

Key Responsibilities

• Lead and conduct high-quality research in the intersection of RL and formal methods, with a focus on mathematical theorem proving.
• Develop and implement novel RL algorithms and models for theorem proving.
• Collaborate with a multidisciplinary team to integrate RL techniques with formal methods.
• Stay abreast of the latest developments in RL, formal methods, and related fields.

Minimum Qualifications

• BS in Computer Science, Mathematics, a related technical field, or equivalent industry experience.
• Demonstrated track record in developing novel and impactful reinforcement learning systems.
• Strong programming skills in Python, with experience in software development and testing.
• Experience in deep learning frameworks such as PyTorch.
• Strong understanding of mathematical concepts, including algebra, geometry, and analysis.

Preferred Qualifications

• MS or PhD in Computer Science, Mathematics, or a related field.
• Experience in applying RL to solve practical problems in formal methods.
• Proven track record of high-quality research demonstrated by publications, patents, or software contributions.
• Contributions to open-source projects or development of software tools in the field.
• Strong background in RL, particularly in areas relevant to theorem proving (e.g., machine learning, natural language processing).
• Proficiency in formal methods, including experience with theorem proving systems.