Physical AI will decide the balance of power for the next century. It runs on components built by Original Component Manufacturers at the base of America's most critical supply chains. The bottleneck isn’t workforce, it’s workflow, which AIC solves by deploying our software operating system to unlock trapped capacity in the companies we acquire by empowering the talented operators who have built those enterprises.

AIC is a software-led industrial holding company. We built our operating system on a proprietary framework called Decision Physics™. Our team deploys on-site to modernize the decision-making infrastructure that changes what a factory can do. We don’t sell SaaS.

Our mission is to secure America's position as the global leader in physical AI. We call ourselves Software Industrialists. We run toward complexity, not away from it. If that's how you see the world, AIC is where you belong.

*Please note this is an onsite role. It will be based out of our Brooklyn Navy Yard HQ or El Segundo office 5x a week.

What you'll do:

• Embed on-site with design partners and manufacturers for meaningful stretches of the year — their floor is your second office.

• Build trust across the full vertical, from the GM to the technician running the station. Software and hardware deployed without this trust doesn't get adopted.

• Develop genuine understanding of how each partner's shop works and why — the history, constraints, people, and physics of what moves through it.

• Build models on factory data. You'll be extracting reliable signal from sparse, messy data, and need to know when regression beats a neural net. You'll ship ML that touches hardware: vision for inspection, part identification, document capture and parsing, models over sensor and machine data, and systems that run on the floor.

• Quantify the connection between operational areas for improvement and EBITDA: which changes on the floor move throughput, quality, margin, business development, and by how much.

• Translate unstructured observation into concrete requirements, then build, ship, and maintain the software yourself and through the other technical staff you coordinate on-site and at HQ.

• Write production code across the full stack — data model, ETL, backend, frontend, algorithms, hardware integrations, ML pipelines, analyses, and infrastructure. Everyone wears all the hats at various points in time.

• Run the process-engineering side of deployment — sequencing software changes with operational changes so the two evolve in lockstep, including the cultural work: training, change management, work instructions, quiet one-on-ones with skeptics.

• Own outcomes, not deliverables. Success is measured in operational maturity and financial outcomes gained by the partner, not features shipped.

Who you are:

• 2+ years of experience in machine learning and software engineering in a fast and collaborative production environment

• Statistical depth and intuition for small data: classical statistics, Bayesian methods, uncertainty quantification.

• Experience taking models from notebook to production: feature pipelines, evaluation, monitoring, and retraining

• Experience with computer vision, time-series and sensor data, causal inference or econometrics, optimization, operations research, applied math, data modeling, ETL, hardware integration, or system and API design

• Strong communication/stakeholder management skills

• Knowledge and intuition for designing and implementing data intensive applications

• Experience with data modeling, ETL, system and API design, frontend, algorithms, hardware integration, data science, applied math, operations research, statistics, machine learning, devOps, or infrastructure

• Hard-tech, robotics, and manufacturing background is a big plus

What we have:

• Medical, Dental, Vision benefits

• Unlimited PTO Policy

• $180-$250k + equity

Nice to Have

• Graduate education in computer science, statistics, applied mathematics, applied physics, economics, electrical engineering, mechanical engineering, or a related field

• Experience in economics or econometrics is a big plus

• ML work with a vision or hardware component, or in robotics-adjacent domains, is a big plus

• Shipped ML systems or public work we can look at: products, papers, open-source, or write-ups

• Track record leading technical projects, especially in a customer-facing role

• Previous startup experience