Product Manager, Simulation & Digital Twin

Product Manager, Simulation & Digital Twin

Sunnyvale, CA (Onsite) | Product

About Noble Machines

Noble Machines builds multipurpose robots to support human workers in the world’s toughest jobs — turning dangerous work from a necessity into a choice. Our robots operate in construction, mining, and energy, where reliability, robustness, and readiness for the unexpected aren’t features; they’re the product. We’re a mission-driven team [Series X / N people — fill in] focused on delivering real impact in heavy industry. If you want to build rugged, reliable products that solve real-world problems, we’d love to talk.

About the Role

You’ll own Noble Machines’ simulation product: the platform where our engineers and our customers test, evaluate, and validate AI models and workflows before they run on a robot at a jobsite.

The role sits between two engineering groups. AI engineers need the simulation to be a fast, faithful, measurable testbed for their models. Simulation software engineers build the platform itself — physics, assets, scenario tooling, evaluation infrastructure. Your job is to understand what each side needs, turn it into requirements, workflows, pipelines, and milestones the other side can build against, and own the sequencing when those needs compete. You’ll also make the same platform legible and usable to customers evaluating Noble Machines on their own sites.

This is a hands-on, high-ownership role on a small team; you’ll set the roadmap and also write the specs.

What You’ll Do

• Own the roadmap and priorities for Noble Machines’ simulation product, across internal engineering use and customer-facing evaluation, from scope and sequencing through delivery.
• Work with AI engineers to define what the platform must provide for model development and validation: scenario coverage, evaluation tasks and metrics, testing workflows, and the data pipelines that feed training and evaluation.
• Work with simulation software engineers to define the platform roadmap: fidelity, asset pipelines, scenario authoring, evaluation infrastructure, and the scale and performance the platform needs to hit.
• Write specs and milestone plans that both groups build against without ambiguity, and hold the sequence when priorities conflict.
• Run structured discovery with customers and prospects to understand how they evaluate autonomy vendors, what evidence moves a pilot to a deployment, and what they need to see from a simulation before they trust it.
• Build digital twins from customer requirements: work with customers, deployment, and customer-facing teams to capture each site’s operating conditions — terrain, hazards, lighting, weather, the failure modes we actually see — and turn them into reproducible simulation assets and scenarios.
• Make the platform usable by customers: define the workflows a customer follows to test and validate an AI model or workflow against a twin of their site, and support evaluations and pilots directly.
• Own customer training and documentation: onboarding, workflow guides, and reference material that let customers run evaluations on the platform without hand-holding.
• Define and own the platform’s success metrics — engineering adoption, customer time-to-first-evaluation, pilot conversion, and the gap between simulation and field results — and organize the roadmap around moving them.

What Success Looks Like in the First Six Months

• A written platform roadmap that AI engineering and simulation software engineering have both signed onto, with sequenced milestones and explicit dependencies.
• AI engineers run model evaluation in simulation as a default step in their workflow, against a defined set of tasks and metrics that the team has agreed constitute “validated.”
• A documented scenario library tied to real field deployments, with coverage gaps identified and prioritized.
• At least one customer has used the platform to evaluate or validate a model or workflow against a digital twin of their site, using documentation and training you own.
• The sim-to-real gap is measured, and the roadmap is organized around closing it.

What You’ll Need

• 5+ years in product management or a hybrid product/engineering path, with direct ownership of a technical product used by engineers — simulation, testing, developer tooling, ML infrastructure, or data platforms.
• Enough fluency in robotics or autonomy simulation to have a substantive technical conversation with the engineers building it — how a simulated environment maps to real-world physical behavior, and where it doesn’t.
• Enough fluency in ML model evaluation to define, with AI engineers, what tasks and metrics make a learned policy “validated.”
• Comfortable serving two very different user groups — internal engineers and external customers — on the same product, and able to say no to one without losing the other.
• Ability to gather requirements from external stakeholders — customers, field teams — and turn ambiguous input into a prioritized, structured spec.
• Strong technical writing and cross-functional communication; documentation that engineering acts on without back-and-forth.
• Bachelor’s degree in Computer Science, Robotics, Engineering, or a related field, or equivalent experience.
• Based onsite in Sunnyvale, CA.

Nice to Have

• Prior experience at an early-stage robotics, autonomous vehicle, or industrial automation company.
• Hands-on experience with a robotics simulation platform (Isaac Lab / Isaac Sim, MuJoCo, Gazebo) or robotics asset formats (USD, URDF, MJCF).
• Background in synthetic data generation, sim-to-real transfer, or digital twin technology.
• Hands-on experience with humanoid, legged, or field robotics.
• Familiarity with heavy industry environments — construction, mining, or energy.

California Pay Transparency Statement

Pay Transparency & Compensation In accordance with California’s Pay Transparency Act (SB 1162), the expected base salary range for this position located in Sunnyvale, CA is $180,000 – $210,000 per year.

Actual compensation within this range will be determined based on several factors, including the candidate’s qualifications, relevant experience, technical skills, and specialized expertise. Base salary is just one component of Noble Machines’ total rewards package, which may also include equity options, comprehensive healthcare benefits, retirement plan contributions, and performance-based incentives.

Equal Opportunity Employer

Noble Machines is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, or any other status protected by applicable federal, state, or local laws.