| name | simulation-software-engineer |
| description | Simulation systems: discrete-event and continuous-time physics/kinematics, sensor and environment
models, digital twins, scenario runners, SIL/HIL interfaces, real-time and faster-than-real-time
execution, deterministic replay, calibration/validation, Monte Carlo sweeps, distributed sim
concepts. Robotics, training sim, automotive, manufacturing, game-engine pipelines (NDA-safe).
Use for simulation software, build a simulator, discrete event simulation, physics simulation,
digital twin, SIL, HIL, scenario runner, simulation framework, deterministic replay, Monte Carlo
simulation, sensor model simulation, Simulink-style, real-time simulation—not game-only, fusion-only
(sensor-fusion-engineer), autonomy stack (tactical-ai-autonomy-developer), MCU firmware
(embedded-real-time-software-engineer), plant control (control-software-developer), formal proofs
(software-assurance-formal-methods-specialist), HIL security (hardware-in-the-loop-security-tester),
server perf only (performance-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
| Need | Skill |
|---|
| Sensor fusion, estimation, track logic | sensor-fusion-engineer |
| Autonomy product implementation and delivery | tactical-ai-autonomy-developer |
| MCU/RTOS firmware, drivers, WCET on embedded targets | embedded-real-time-software-engineer |
| DCS/PLC control applications and OT integration | control-software-developer |
| HIL security assessment, bus injection for security | hardware-in-the-loop-security-tester |
| Formal methods, proof, assurance cases | software-assurance-formal-methods-specialist |
| Server/UI performance profiling and load tests | performance-engineer |
| OT/ICS plant security and operations | scada-ics-cyber-security-specialist |
| Pre-flight architecture/security/cost validation | build-validator |
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
| Topic | Reference |
|---|
| Role boundaries, paradigms, domains | references/simulation_software_scope.md |
| DE/CT time, physics, integrators | references/modeling_time_and_physics.md |
| Sensors, environment, scenarios | references/sensors_environment_and_scenarios.md |
| Real-time, determinism, replay | references/execution_realtime_and_determinism.md |
| Calibration, validation, metrics | references/validation_calibration_and_metrics.md |
| SIL/HIL, digital twins, integration | references/integration_sil_hil_and_twins.md |
