Member of Technical Staff, Product

Arena Physica is on a mission to accelerate hardware innovation that powers human progress. Our name is inspired by Theodore Roosevelt's 'Citizenship in a Republic' speech. To us, entering the Arena means committing fully and accepting the risk of failure in pursuit of an audacious, worthy cause. We believe the future belongs to those brave enough to build it.

Our team of 50 combines AI engineering and applied physics expertise with deep experience in enterprise deployments. We're headquartered in NYC with presences in San Francisco and Los Angeles, backed by ~$90M from Initialized, Founders Fund, Goldcrest Capital, Fifth Down Capital, and Shield Capital.

If you're ready to do the most important work of your career, join us in the Arena.

At Arena Physica, we're building electromagnetic superintelligence. Our AI platform Atlas operationalizes physics-grounded intelligence to verify, debug, and optimize hardware across its lifecycle. Atlas is already trusted globally by the world's most advanced hardware companies, including AMD, Anduril, and Bausch & Lomb, for applications across R&D, integration testing, production assembly, and field repair.

As a Member of Technical Staff focused on Agentic Reasoning & Core Capabilities, you will build the systems that allow Atlas to reason, plan, use tools, inspect evidence, run workflows, and help hardware engineers solve problems that previously required deep, manual expert intervention. You will work across the model, product, and platform layers to make Atlas more capable, reliable, and useful in real engineering environments.

This role is for a high-agency engineer who wants to own ambiguous, frontier capabilities end-to-end. You will turn expert electrical engineering workflows into agentic systems: decomposing problems, retrieving context, selecting tools, running simulations or analyses, checking intermediate results, and producing decisions that hardware teams can trust.

• Build Atlas's reasoning core - Design and implement agentic planning, tool use, memory, retrieval, self-checking, and workflow execution capabilities for hardware verification, debugging, and optimization.
• Turn expert workflows into an agentic harness - Translate EM, RF, signal integrity, power integrity, lab validation, simulation, and field-repair processes into reusable agent capabilities that work across real customer environments.
• Improve model behavior in production - Develop systems that make Atlas more reliable at following long-horizon tasks, asking the right clarifying questions, grounding answers in evidence, and recovering when tools or assumptions fail.
• Create domain-specific evaluations - Build evals, graders, traces, and diagnostics that measure agent performance on hardware engineering tasks rather than generic chatbot benchmarks.
• Connect models to tools and data - Integrate LLMs, electromagnetic foundation models, retrieval systems, solvers, lab data, design artifacts, and engineering systems of record into coherent end-to-end workflows.
• Ship with customers in the loop - Partner with Product, Go-To-Market, and forward-deployed engineering teams to learn from real deployments and rapidly convert feedback into stronger reasoning systems and core capabilities.
• Travel domestically and internationally (10-20% of your time).
• Work in person at Arena's NYC HQ when not traveling.

• 5 years of software engineering, research engineering, or ML systems experience at a venture-backed startup, top technology company, frontier AI lab, or similarly demanding environment.
• Demonstrated experience building LLM-powered systems, agentic workflows, model tooling, evals, retrieval systems, or production AI products.
• Strong software engineering fundamentals and the ability to move fluidly between prototypes, infrastructure, product surfaces, and production services.
• Excellent judgment around model behavior, user experience, reliability, observability, and the failure modes of AI systems.
• Comfort operating in ambiguous problem spaces where the right abstraction is discovered through iteration with users, data, and model behavior.
• Ability to collaborate deeply with researchers, product engineers, electrical engineers, and customer-facing teams.
• Strong communication skills and a track record of driving complex technical projects from idea to shipped capability.
• Self-directed ownership mindset and excitement for building core systems at the frontier of AI and hardware engineering.
• [Preferred] Experience with tool-using agents, coding agents, computer-use agents, multi-agent systems, RL/post-training, synthetic data generation, harness engineering, or automated evals.
• [Preferred] Experience building AI systems for technical users such as engineers, researchers, analysts, developers, or operators.
• [Preferred] Familiarity with hardware engineering, electromagnetic simulation, RF systems, signal integrity, power integrity, EDA tools, lab instrumentation, or manufacturing workflows.
• [Preferred] Interest in building AI that operates in the physical world, where correctness, evidence, and engineering judgment matter.

• 100% of the monthly premiums covered with Aetna medical vision, and dental insurance for you and your dependents
• 401(k) Retirement Plan
• Unlimited PTO
• Lunch every day from local restaurants via Sharebite
• Relocation support provided