Evaluation Engineer

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