What are the responsibilities and job description for the Research Engineer, Reinforcement Learning position at Harmonic?
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
New York Times Exclusive: Is Math the Path to Chatbots That Don't Make Stuff Up?
Sequoia Capital Training Data Podcast: Why Vlad Tenev and Tudor Achim of Harmonic Think AI Is About to Change Math—and Why It Matters
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
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.
- 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.
- 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.
New York Times Exclusive: Is Math the Path to Chatbots That Don't Make Stuff Up?
Sequoia Capital Training Data Podcast: Why Vlad Tenev and Tudor Achim of Harmonic Think AI Is About to Change Math—and Why It Matters
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
