At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.

The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.

Who We Are

We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.

This role is responsible for building and scaling simulation based autonomy tests by turning requirements and incidents into reproducible pass fail scenarios and KPIs then wire them into CI for fast regression detection clear reporting and release readiness gates while partnering with autonomy controls QA release engineering and ML teams to close coverage gaps reduce flakes and produce metrics dashboards and simulation derived datasets

Responsibilities

• Design and build simulation scenarios representing nominal, edge-case, and adversarial conditions.

• Translate autonomy requirements and real-world incidents into reproducible tests with clear, measurable outcomes.

• Script environments, actors, triggers, and behaviors in Isaac Sim or Unreal Engine.

• Maintain configuration-driven scenario setups (YAML/JSON) for reproducibility, sweeps, and domain randomization.

• Develop Python utilities and Bash scripts to automate scenario generation, execution, and evaluation.

• Integrate simulation-based tests into GitHub-based CI pipelines (PR checks, nightlies, release validation).

• Improve reliability, scalability, and visibility of scenario execution, including logging and metric collection.

• Implement and refine system-level KPIs (safety constraints, planner success, tracking stability, interactions).

• Analyze simulation results to detect regressions, understand multi-module behaviors, and surface meaningful insights.

• Support ML dataset creation from simulation and resimulation, including metadata curation and edge-case identification.

• Work closely with autonomy, controls, QA, and release engineering to align scenarios with feature changes and test requirements.

• Participate in debugging sessions, test planning, and design reviews to identify issues and propose improvements.

• Document scenarios, evaluation logic, and test plans clearly for internal stakeholders.

• Take increasing ownership of scenario domains, test frameworks, or automation pipelines.

• Influence simulation test strategy and standards through strong technical judgment.

• Help mentor teammates, improve processes, and drive best practices around system-level testing.

Qualifications

• Bachelor’s degree in computer science or engineering, or equivalent work experience of 4-6 years.

• 4-6 experience in robotics, autonomous systems, simulation, or system testing (range appropriate for intermediate–senior).

• Strong Python skills, including automation, tooling, or testing frameworks.

• 2-4 years Hands-on experience with Isaac Sim, Unreal Engine, Gazebo, or similar simulation environments.

• Knowledge of config-driven workflows (YAML/JSON) and scripting (Bash).

• Ability to debug multi-module system behavior using logs, metrics, and visualization tools.

• Familiarity with CI/CD workflows and GitHub-based development.

What Makes You Stand Out

• Background in perception/tracking, planning, controls, or sensor simulation.

• Experience converting logs or incidents into simulation scenarios.

• Exposure to dataset creation, annotation tooling, or ML evaluation pipelines.

• Familiarity with C++ or GPU concepts for simulation performance (not required).

• Understanding of system safety, ODD definition, or scenario-based safety frameworks.

Something Little Extra

• Comfortable owning scenario development from concept through automated evaluation.

• Operate in terms of system behavior, interactions, and measurable validation.

• Have built scalable simulation, testing, or automation systems.

• Document thoughtfully and communicate clearly across teams.

* Please note: The base salary range listed in this job description reflects compensation for candidates based in the San Francisco Bay Area. We are also open to qualified talent working remotely across the:

United States - Base salary range (U.S. – all locations): $110k-$130k USD