Evaluation Engineer

Maven Robotics, Inc.; Santa Clara (CA); On-site; USD 130,000 - 180,000; Full time

Job summary

Maven Robotics, Inc. in Santa Clara, CA is seeking a Robotics Evaluation Engineer to build systems that measure, diagnose, and improve robot policy performance across simulation and real robots.

You will own the core evaluation infrastructure that enables our AI team to rigorously compare models, understand failure modes, measure generalization and reliability, and make better decisions about data, training, and deployment.

Qualifications

• Significant experience building robotics evaluation, simulation, testing, or benchmarking systems.
• Strong programming ability in Python, C++, or similar robotics or systems languages.
• Experience with robotics simulation platforms such as Isaac Sim, MuJoCo, Gazebo, or similar systems.
• Experience evaluating learned robotic policies in simulation and on physical robots.
• Experience designing reproducible experiments, metrics, benchmarks, and failure analysis workflows.

Responsibilities

• Own the architecture, implementation, and evolution of Maven’s robot learning evaluation framework across simulation and real robots.
• Define task specifications, success criteria, failure taxonomies, evaluation metrics, and benchmark protocols for manipulation policies.
• Build automated systems for comparing models, detecting regressions, running large-scale evaluations, and generating clear performance reports.
• Develop simulation environments, task scenarios, and procedural variations used for policy evaluation.
• Build reproducible real-robot evaluation workflows that minimize manual supervision and produce statistically meaningful results.
• Measure and improve the relationship between simulated and real-world policy performance.
• Build tools for replaying, visualizing, and diagnosing policy failures across simulation and physical robots.

Skills

Robotics evaluation

Python/C++

Simulation frameworks

Benchmarks

Data analysis

Tools

Isaac Sim

MuJoCo

Gazebo

Job description

Maven Robotics designs, builds, and deploys intelligent robotics for logistics and manufacturing. Maven is building the first general‑purpose robotics system for industrial work. Our mission is to activate autonomous labor at scale and build toward infinitely elastic industrial capacity. We’re a team of engineers, researchers, and operators who have spent our careers working on the hardest problems in AI, robotics, engineering, and autonomous systems. We work at the intersection of advanced research and real‑world engineering, turning ideas into prototypes and deployed systems.

At Maven, we build around the realities of industrial work. The robots are designed from first principles for complex physical environments and rigorous commercial standards. They master real tasks that deliver immediate impact, industrial‑grade reliability, and intelligence that compounds at scale. We’re growing an ambitious team of people who think in systems, move quickly, and are builders at heart.

About the Role

As a Robotics Evaluation Engineer at Maven Robotics, you will build the systems that measure, diagnose, and improve robot policy performance across simulation and real robots. You will own the core evaluation infrastructure that enables our AI team to rigorously compare models, understand failure modes, measure generalization and reliability, and make better decisions about data, training, and deployment.

You will work closely with robot learning researchers and engineers across simulation, perception, controls, data, and infrastructure to define task success, build reproducible evaluation frameworks, automate testing, and develop the tools needed to understand why robot policies succeed or fail.

What You’ll Do

• Own the architecture, implementation, and evolution of Maven’s robot learning evaluation framework across simulation and real robots
• Define task specifications, success criteria, failure taxonomies, evaluation metrics, and benchmark protocols for manipulation policies
• Build automated systems for comparing models, detecting regressions, running large‑scale evaluations, and generating clear performance reports
• Develop simulation environments, task scenarios, and procedural variations used for policy evaluation
• Build reproducible real‑robot evaluation workflows that minimize manual supervision and produce statistically meaningful results
• Measure and improve the relationship between simulated and real‑world policy performance
• Build tools for replaying, visualizing, and diagnosing policy failures across simulation and physical robots
• Work closely with robot learning researchers to identify whether failures originate from the model, data, perception, controls, calibration, simulation, hardware, or long‑horizon execution

What We’re Looking For

• Significant experience building robotics evaluation, simulation, testing, or benchmarking systems.
• Strong programming ability in Python, C++, or similar robotics or systems languages
• Experience with robotics simulation platforms such as Isaac Sim, MuJoCo, Gazebo, or similar systems
• Experience evaluating learned robotic policies in simulation and on physical robots
• Experience designing reproducible experiments, metrics, benchmarks, and failure analysis workflows
• Ability to build reliable software systems for automated testing, data collection, visualization, and reporting
• Strong understanding of robotics systems and the ability to debug issues across software, models, sensors, controls, and hardware
• A track record of independently owning complex technical projects from initial design through deployment and ongoing operation

Nice to Have

• Familiarity with sim‑to‑real transfer, domain randomization, sensor modeling, and system identification
• Experience importing and configuring simulation assets, including robot models, objects, joints, collisions, inertial properties, materials, and sensors
• Experience with hardware‑in‑the‑loop testing, continuous integration for robotics, or automated robot test cells
• Familiarity with multimodal robot policies, vision‑language‑action models, or other learned manipulation architectures
• Strong background in statistical analysis, confidence estimation, and experimental design

Maven Robotics is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Voluntary Self‑Identification

For government reporting purposes, we ask candidates to respond to the below self‑identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

As set forth in Maven Robotics’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows:

A “disabled veteran” is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service‑connected disability.

A “recently separated veteran” means any veteran during the three‑year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.

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An “Armed forces service medal veteran” means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.