Research Engineer: RL & Formal Methods for Theorem Proving

Harmonic Security; Palo Alto (CA); Sur place; USD 150 000 - 210 000; Plein temps

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Unlimited PTO

401(k) matching

Health, vision, and dental benefits (0

Résumé du poste

Harmonic is seeking a Research Engineer to advance mathematical theorem proving using cutting-edge RL techniques within our Reinforcement Learning & Formal Methods team.

You will develop new algorithms, implement models, and collaborate across disciplines to integrate RL with formal methods for challenging proof problems and beyond.

Join a technically elite team at Harmonic, focused on rigorous verification and scalable reasoning in AI systems.

Qualifications

• BS or MS in Computer Science, Mathematics a related technical field, or equivalent industry experience
• 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.

Responsabilités

• 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.

Connaissances

Python programming

Deep learning (PyTorch)

Mathematical reasoning

RL basics

Formation

BS/MS in CS/Math or related field

PhD preferred

Outils

PyTorch

Git

Description du poste

Harmonic is seeking a Research Engineer to advance mathematical theorem proving using cutting-edge RL techniques within our Reinforcement Learning & Formal Methods team.

You will develop new algorithms, implement models, and collaborate across disciplines to integrate RL with formal methods for challenging proof problems and beyond.

Join a technically elite team at Harmonic, focused on rigorous verification and scalable reasoning in AI systems.