5-7 years of experience in robotics evaluation, simulation, or benchmarking systems
Proficient in programming languages such as Python or C++
Familiarity with robotics simulation platforms like Isaac Sim or Gazebo
Experience in evaluating robotic policies in both simulation and real-world settings
Skilled in designing reproducible experiments and metrics
Ability to develop reliable software for automated testing and data visualization
Proven track record of managing complex technical projects from design to deployment
Responsibilities
Own the architecture and evolution of the robot learning evaluation framework
Define task specifications and success criteria for manipulation policies
Build automated systems for model comparison and regression detection
Develop simulation environments and task scenarios for policy evaluation
Create reproducible workflows for real-robot evaluations
Measure and enhance the correlation between simulated and real-world performance
Develop tools for diagnosing policy failures across various platforms
Benefits
Collaborative work environment with leading robotics researchers
Opportunity to influence the development of cutting-edge robotics technology
Access to advanced robotics simulation tools and platforms
Engagement in complex, impactful projects that drive innovation
Potential for professional growth and skill enhancement in a rapidly evolving field
Full Job Description
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
