Product Manager, Machine Learning
Role Overview
The hardest part of Roboforming is generating a path that will produce the highly accurate part 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 run cost material, labor, and robot time. To make our manufacturing accessible to broader consumer markets we need to reach the objective part tolerance in fewer trials. We get there by predicting springback before we form.
Traditional solvers cannot get us there. Off-the-shelf FEAs are built for a handful of known loads, not millions of separate small bends. Instead, we need to learn the behavior. This role owns the products that let us do that: machine learning across all the parts we have formed, and GPU based physics simulations where we simulate complex physics.
Planned Areas of Focus
Springback Prediction
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.
Input Parameter Selection
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.
Data and Simulation Environments
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.
Full Job Posting
About the Role
The hardest part of Roboforming is generating a path that will produce the highly accurate part 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 run cost material, labor, and robot time. To make our manufacturing accessible to broader consumer markets we need to reach the objective part tolerance in fewer trials. We get there by predicting springback before we form.
Traditional solvers cannot get us there. Off-the-shelf FEAs are built for a handful of known loads, not millions of separate small bends. Instead, we need to learn the behavior. This role owns the products that let us do that: machine learning across all the parts we have formed, and GPU based physics simulations where we simulate complex physics.
Planned Areas of Focus
Springback Prediction
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.
Input Parameter Selection
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.
Data and Simulation Environments
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.
Responsibilities
- 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
- What We’re Looking For
- You Have
- 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 proven track record of shipping complex solutions. You define problems before solutions, drive engineering execution, and measure success by outcomes, not output.
- Enough depth to steer technical direction with an ML team — you can debate architecture choices, know what data an approach requires, and recognize when one is a bad bet.
- Deep curiosity about users. You get close to the people using your software and build products that genuinely improve how they work.
- Outstanding communicator, comfortable presenting to executives, influencing teams across the company, and aligning collaborators
- We Prefer
- Prior product experience where a model output is the product
- Direct experience with machine learning or computer vision, whether from coursework, research, or models you built and trained yourself.
- Experience building or using physics-based simulation
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