Senior Software Engineer, Simulation

AeroVect; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

AeroVect is transforming ground handling with autonomous simulation tools. We seek a skilled engineer to own and extend our simulation tooling, focusing on determinism, reproducibility, and scalable test regimes for autonomy stacks.

You will build integrations with production autonomy modules, improve fidelity, and run scenario executions in the cloud, delivering reliable verdicts and actionable metrics for release gating.

Qualifications

• Bachelor’s in Computer Science, Electrical Engineering, Robotics, or related field.
• Strong Python with maintainable code in a shared codebase.
• Hands-on simulation experience for autonomous systems and familiarity with major simulation platforms.

Responsibilities

• Build and extend the simulation environment capabilities and integrations.
• Maintain and evolve integration as the autonomy stack grows.
• Build and operate cloud-based scenario execution, including orchestration and results aggregation.
• Develop evaluation layer to gate releases with precise pass/fail criteria.
• Create failure-triage tooling and dashboards for autonomy engineers.
• Extend scenario authoring tooling for V&V teams across backend and frontend.
• Maintain simulation foundations: determinism, reproducibility, and coverage expansion.

Skills

Python

Distributed systems

Debugging & profiling

CI/CD

Education

Bachelor’s degree in Computer Science/Engineering/Robotics

Master’s degree in Computer Science/Robotics (preferred)

Tools

CARLA

Applied Intuition

Foretellix

NVIDIA Omniverse

IsaacSim

Gazebo

Job description

Who We Are

AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.

You will own and extend the simulation tooling. A significant part of the role is test quality. A test suite that produces flaky failures does not provide usable information. Determinism, reproducibility, and verifying that each gate is capable of failing are ongoing responsibilities rather than one-off tasks.

You Will

• Build and extend the capabilities of the simulation environment: integration with the production autonomy stack, the interfaces it consumes, vehicle and actor models, and the range of environmental and operational conditions that can be represented

• Maintain and improve that integration as the autonomy stack evolves, including fidelity work where the difference between simulated and real inputs changes how the stack behaves

• Build and operate scenario execution in the cloud: orchestration, parallelism, result aggregation, artifact capture, and runtime cost modelling

• Build and extend the evaluation layer that turns a run into a verdict — assertions and pass/fail criteria precise enough to gate a release and stable enough to avoid false failures

• Build failure-triage tooling: failure clustering, per-failure recordings, and dashboards that autonomy engineers can use without assistance

• Extend the scenario authoring tooling used by the V&V team, across backend and frontend

• Maintain simulation foundations: determinism and reproducibility, pipeline performance, and extending coverage to additional maps and sites

You Have

• Bachelor’s in Computer Science, Electrical Engineering, Robotics, or related field

• Strong Python, with a track record of maintainable code in a shared codebase

• Hands-on simulation experience for autonomous systems, familiarity with simulation platforms such as CARLA, Applied Intuition, Foretellix, NVIDIA Omniverse, IsaacSim, Gazebo, or a proprietary in-house simulatorWorking knowledge of simulation, modelling, and validation methodology, including how simulated results are used to support claims about real-world behaviour

• Docker and Linux, distributed-systems fundamentals, and experience with GPU-based simulation environmentsCI/CD and cloud execution, experience integrating automated test workflows into CI for end-to-end validation coverage

• Experience analyzing simulation output and telemetry to identify performance bottlenecks and failure modes

• Debugging and profiling skills suited to distributed, GPU-bound systems

We Prefer

• Master’s in Computer Science, Robotics, or related field

• Experience designing and validating safety-critical systems in autonomous driving, aerospace, or robotics

• Experience developing or maintaining autonomous vehicle software stacks (ROS/ROS2)

• Cloud-based simulation infrastructure and large-scale distributed test execution

• Sensor modelling (camera, lidar, radar), environment generation, or perception ground-truth pipelines

• Automated testing, continuous integration, and data-driven validation

• Log/bag re-simulation from recorded real-world data

• Test-signal quality work: flake reduction, determinism debugging, golden-output comparison

• Safety standards exposure: ISO 26262, ISO 21448 (SOTIF), UL4600, ISO 13849