Research Engineer

AI in this role

pytorch

nlp

About Harmonic

At Harmonic, we are building a mathematical reasoning engine that operates with absolute precision. While most AI makes maximum-likelihood guesses, Harmonic's Aristotle uses Lean4 and reinforcement learning to verify its reasoning and results.

Following our Gold Medal-level performance on the 2025 International Math Olympiad (IMO) and the successful resolution of long-standing open problems, we are proving that AI can master the most rigorous domains of human thought. Backed by some of the world’s most prominent investors, we are intentionally scaling an elite technical team.

Visit our company blog to learn more about what we are working on!

About the Role

We are seeking a highly motivated and skilled Research Engineer to join our Reinforcement Learning & Formal Methods team. This position will be focused on advancing 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

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 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.

Preferred Qualifications

PhD in Computer Science, Mathematics, or a related field.
Experience in applying AI 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.

What We Offer

Unlimited PTO
401(k) matching
100% employer-paid health, vision, and dental benefits for employees and 50% coverage for dependents. Harmonic offers varied health coverage options to select what is best for you and your family.
Health Savings Account (HSA) available for qualifying health plans

Equal Opportunity Statement

Harmonic is committed to diversity and inclusivity in the workplace. 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.

How we rate this

Research Engineer at Harmonic rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Research Engineer is an open job at Harmonic.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $200k-$450k a year.
The role is based in Palo Alto.
The job was posted on July 10, 2025.
Applications are handled by Harmonic. Apply on Harmonic

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

NLPPyTorch

Questions you could be asked

What NLP problem have you worked on, and how did you measure whether it actually worked?
Walk me through how you've used PyTorch in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: NLP and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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