Senior Product Manager, Machine Learning

About Machina:

Engineering moves at software speed. Manufacturing doesn'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're 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.

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 predict 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

$150,000 - $210,000 a year

The base salary range for this role is dependent on experience, qualifications, and overall alignment with the scope of the position.

In addition to base compensation, Machina Labs offers a competitive benefits package and stock option participation.

*This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities required for this role. Duties and responsibilities may change based on business needs.

Machina Labs is an Affrimateive Action and Equal Employment Opportunity employer and considers all applicants for employment without regard to race, color, religion, sex, gender identity, gender expression, sexual orientation, national origin, age, disability, or status as a protected veteran in accordance with state and federal law.

We endeavor to make the job application process accessible to any and all users. If you have a disability that impacts your ability to complete the job application process and would like to request assistance or a reasonable accommodation, please contact us at View phone number on click.appcast.io. This contact information is for accommodation requests only, not to inquire about the status of applications.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.