Simulation Software Engineer

Simulation Software Engineer

When to Use

• Choose discrete-event vs continuous-time (or hybrid) simulation architecture and time-stepping policy
• Build physics and kinematics models—rigid body, vehicle dynamics, simplified aerodynamics, constraints
• Model sensors and environments—noise, bias, latency, occlusion, weather, terrain, traffic, actors
• Design digital twins and scenario runners—parameterized worlds, replay, batch sweeps, regression suites
• Define SIL/HIL interfaces—stimulus injection, plant models, clock sync, I/O mapping, fault injection hooks (engineering, not security bench)
• Engineer real-time vs faster-than-real-time execution—scheduling, back-pressure, wall-clock coupling
• Implement deterministic replay—seed control, ordering, floating-point policy, record/playback contracts
• Run calibration and validation against logs, flight/field data, or bench measurements with explicit metrics
• Plan Monte Carlo and parameter sweeps—sampling design, coverage, aggregation, failure taxonomy
• Outline distributed simulation—federation concepts, time management, bandwidth/latency budgets (concept level)
• Integrate game-engine or middleware pipelines when simulation rigor (time, sensors, replay) is required

When NOT to Use

• Pure game development without simulation rigor (determinism, validation, SIL/HIL, sensor truth models) → game/graphics skills as appropriate
• Sensor fusion algorithms only—Kalman/graph optimization, track association, estimator tuning without building the sim stack → sensor-fusion-engineer
• Autonomy product stack—perception/planning product code, fleet ops, tactical autonomy delivery → tactical-ai-autonomy-developer
• Bare-metal MCU firmware, ISR/RTOS on chip, driver bring-up → embedded-real-time-software-engineer
• Industrial plant control applications—DCS/PLC scan cycles, OPC UA to historians, BPCS logic → control-software-developer
• Formal proof obligations—theorem proving, certified code, assurance case ownership → software-assurance-formal-methods-specialist
• HIL security testing—authorized bus fault injection, exploit benches, penetration on hardware rigs → hardware-in-the-loop-security-tester
• Service-level profiling, load tests, p99 on servers or browsers without simulation architecture → performance-engineer

Related skills

Core Workflows

1. Scope, paradigms, and success criteria

Define simulation purpose (V&V, training, design exploration, digital twin), fidelity tiers, and measurable acceptance metrics.

See references/simulation_software_scope.md.

2. Time bases, physics, and numerical stability

Select DE/CT/hybrid time management, integrators, stiffness handling, and coordinate frames.

See references/modeling_time_and_physics.md.

3. Sensors, environment, and scenarios

Model sensing pipelines, world state, actors, and scenario DSL/runner contracts.

See references/sensors_environment_and_scenarios.md.

4. Execution, real-time, and determinism

Schedule sim loops, real-time coupling, record/playback, seeds, and reproducibility policies.

See references/execution_realtime_and_determinism.md.

5. Validation, calibration, and metrics

Compare sim to measured data; tune parameters; report uncertainty and regression gates.

See references/validation_calibration_and_metrics.md.

6. SIL/HIL, twins, and integration

Map software-in-the-loop and hardware-in-the-loop boundaries, clocks, I/O, and twin synchronization.

See references/integration_sil_hil_and_twins.md.

Outputs

• Simulation architecture brief — paradigm (DE/CT/hybrid), time policy, modules, fidelity tiers, risks
• Model catalog — physics, sensors, environment, interfaces, units, assumptions, known gaps
• Scenario specification — parameters, initial conditions, termination, pass/fail metrics, seed policy
• Determinism and replay contract — what is logged, ordering rules, FP policy, version pins
• SIL/HIL interface sheet — signals, rates, latency, clock domains, fault injection points (engineering)
• Validation report — metrics vs ground truth, calibration parameters, residual analysis, regression suite
• Sweep/Monte Carlo plan — sampling design, coverage matrix, aggregation and failure taxonomy
• Distributed sim concept note — federation roles, time management, bandwidth budget (when applicable)

Principles

• Match fidelity to decision — coarse models for exploration; high fidelity only where metrics demand it
• Make time explicit — document clocks, step sizes, event ordering, and real-time coupling assumptions
• Separate truth, sensor, and estimator — ground truth in sim ≠ sensor output ≠ fusion output
• Invest in reproducibility — seeds, deterministic builds, pinned assets, replay contracts before scaling sweeps
• Validate against measurements — calibration is incomplete without stated metrics and holdout data
• Keep security and formal peers in lane — route HIL security and proof obligations to named skills
• Stay NDA-safe — generic patterns only; no contractor names, controlled data, or export-sensitive payloads

When to load references