You’ll work directly alongside our Principal AI Engineer, who sets the technical direction for the system. Your job is to be an implementation and experimentation partner on the agentic system: the person who can take a research idea and turn it into a runnable experiment, interpret what the results mean, and help decide what to try next. As the system matures, the role will evolve toward owning the production architecture on AWS. The key responsibilities of the job are as follows:

Design and run experiments to evaluate what works: tool structures, reasoning patterns, retrieval approaches, optimization integrations. Interpret what the results are telling you about the system’s design.
Build fast, readable prototypes that are easy to modify and discard when an experiment doesn’t pan out.
Work iteratively with the team to figure out what the right agentic architecture is, not just how to implement a predetermined one.
Diagnose failures clearly, distinguishing engineering problems from prompt design problems from domain modeling problems.
Communicate findings clearly to a technically deep team that includes optical scientists and engineers.
Grow into ownership of the production architecture on AWS as the system matures.

The ideal candidate is an AI engineer with a physics, EE, or applied math background. Someone with genuine hands-on experience building agentic systems and a strong quantitative foundation to work productively alongside a team of optical scientists and engineers. You don’t need to be an optical designer; that expertise exists on the team. What matters is that you’re not intimidated by the domain, can engage with it meaningfully, and bring AI and agentic systems depth.

Required Qualifications

Bachelor’s degree in physics, EE, or applied math
5+ years’ post-education work experience
Hands-on experience building agentic or LLM-integrated systems. You have debugged a broken tool loop, managed multi-turn state, and worked with at least one agentic framework (LangGraph, Strands Agents, Claude Agent, or similar)
Production-quality Python. Not notebook Python, but code that handles errors, is testable, and can be read by someone else six months from now
Meaningful AWS experience. You have built and operated real systems on AWS, understand core services (ECS, Lambda, S3, IAM, SQS, or related services), and have debugged things that broke in production
A demonstrated ability to design informative experiments. You know the difference between an experiment that answers a question and one that just generates activity
Comfort operating without a complete roadmap. You can make good decisions under ambiguity and know when to ask versus when to try
US Citizenship

Preferred Qualifications

Master’s degree in physics, EE, or applied math
Familiarity with optical design concepts or software (e.g., Zemax, Code V)
Exposure to reinforcement learning concepts
Experience with MATLAB or numerical computing workflows
Background in advanced mathematics: optimization theory, numerical methods, linear algebra, or differential equations

About Peak Nano

Established in 2016 to bring patented nanotechnology from the laboratory to commercial applications, Peak Nano is tackling challenges across the power grid, fusion, electric vehicles, aerospace, and defense. With AI-powered design and advanced nanolayered technology, Peak Nano’s drop-in-ready, industry-disrupting solutions dramatically boost systems’ performance.

Our NanoPlex™ films technology, protected by 20+ global patents, is designed and engineered in the U.S., with a secure supply chain from allied nations, reducing dependence on foreign suppliers. These purpose-built nanolayered solutions enable breakthroughs across critical industries, strengthening American energy independence, leadership, and national security.

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