Product Manager, Machine Learning

Engineering moves at software speed. Manufacturingdoesn't. Yet.

Machina Labs is changing that. We build intelligent, software-defined factories that produce complex metal structures directly from digital design. By integrating advanced metal forming, robotics, and automated production inside a flexible factory architecture, we enable customers to move from prototype to production in weeks, not years.

Backed by Lockheed Martin, Toyota, and NVIDIA,we'rebuilding the manufacturing infrastructure that defense, aerospace, and advanced mobility programs will run on.

If you want to work on hard problems that matter and see them fly, drive, and defend, this is the place.

The hardest part of Roboformingis generatingapath that will produce the highlyaccuratepart you set out to make. The metal does not want to be formed. It springs back the instant the tool moves past it, and once formed it is packed full of residual stress that wants to pull the part out of spec at any change in environmental conditions. The path we generate is never the part we get, so the real task is finding the path that delivers the part we are after.

Today we find that path by iterating. We form a part, measure the error, and adjust the path to compensate. This works, but it is expensive. Every runcostmaterial, labor, and robot time. To make our manufacturing accessible tobroaderconsumer markets we need to reachthe objective part tolerance in fewer trials. We get there by predictingspringbackbefore we form.

Traditional solvers cannot get us there.

Off-the-shelfFEAsarebuilt for a handful of known loads, not millions of separate small bends.

Instead, weneed to learnthe behavior. This role owns the products that let us do that: machine learning acrossall the partswe have formed, and GPU based physics simulations where we simulate complex physics.

You will own the models that predicts how the part will spring back and pre-compensates the path, so the very first formed part lands close to the target shape.

You will own the model that tells users which input parameters to pick during path planning, so the part forms optimally. This process is currently dependent on experience and tribal knowledge in a way that doesn’t scale.

You will own the data sets necessary to train all ML models. This includes exploring historic data as well as working closely with the R&D team to plan experiments and runs necessary to fill in gaps in the data.

• Own the machine learning and simulation roadmap. Define priorities, make tradeoffs, and communicate direction to engineering and leadership.

• Run sprints for the Machine Learning/Physics Sim team and drive delivery from discovery through release

• Conduct direct user research with internal process engineers to deeply understand their needs and how ML can help their workflow

• Write clear product requirements that engineers can build from, with enough technical depth to have substantive tradeoff conversations

• Become the internal expert on what happens at the cell and how that behavior can be modeled: what the robots are doing, why material type matters, and which predictions would actually change a user's decision.

• Participate in company roadmap and strategy discussions, representing the software tools perspective

• Partner with the Product Lead to align the machine learning roadmap with broader company priorities

• A bachelor's degree in computer science, electrical engineering, or a related engineering field. Or equivalent practical experience demonstrating the same depth

• 5+ years of product management experience in technical software environments

• A…