Founding Simulation Engineer
Turn simulation concepts into reliable, reusable and scalable simulation infrastructure.
This role sits between ML and product engineering. You take a simulation idea and make it operational: designing execution flows, state transitions, orchestration, data structures and runtime systems so simulations behave consistently at scale.
The role
The Founding ML Engineer focuses on models, cognition engines, calibration, inference and learning systems. The Founding Product Engineer focuses on customer-facing products, workflows, interfaces, white-labeling and enterprise features.
The Founding Simulation Engineer connects them. You turn simulation concepts into simulation infrastructure.
What you would own
Reusable architectures for product interaction, behavioral journey, ad, messaging, multi-agent, pricing and enterprise simulations
Executable simulation flows: environment setup, audience initialization, stimulus exposure, branching, stopping conditions, outputs and reproducibility
Agent orchestration: state assignment, model calls, sequencing, context, interaction, memory and event recording
State and environment modeling: stimuli, event propagation, timing, feedback loops, interaction rules and persistent history
Reusable schemas for audiences, environments, tasks, constraints, behavioral parameters and output metrics
Observability through event streams, traces, timelines, decision logs, failure diagnostics, latency and cost monitoring
Problems you might solve
Turn a 20-agent research prototype into a standardized workflow that can run thousands of times
Coordinate 500 simulated individuals without every agent needing to communicate with every other agent
Handle abandonment, hesitation, retries, changing minds and social influence
Scale agent runs through parallel execution, asynchronous processing, queues, batching, retries and worker systems
Create reusable primitives, including stimulus, environment, audience, decision event, interaction, observation, state transition and outcome
Reproducibility
A major responsibility is making simulations inspectable rather than arbitrary. You will build around seeds, configuration, versioning, deterministic components, experiment tracking, logs, replayability and simulation snapshots.
Researchers and customers should be able to understand what changed between two simulation runs.
Technical profile
Python and/or Rust
Distributed systems, backend engineering and asynchronous programming
Event-driven architectures, workflow engines, queues and state machines
Databases, APIs, LLM orchestration and cloud infrastructure
Simulation systems and agent systems
Game simulation, multi-agent systems, robotics, event simulation, distributed computing or RL environments are especially interesting
What this is not
This is not primarily frontend development, prompt engineering, model training, individual customer features or isolated scripts. The role exists to prevent Aetherya’s simulation technology becoming a collection of disconnected prototypes.
Compensation & involvement
This is a founding-team, equity-compensated position with an indicative grant of approximately 1.0%, depending on ability, commitment, ownership, experience, long-term involvement and contribution to core infrastructure. Vesting and final legal documentation apply. There is currently no cash salary.
We expect substantial, consistent involvement, approximately 20+ hours per week with flexibility around individual circumstances.
What success looks like
Reusable simulation primitives and standardized configuration
Scalable agent orchestration and distributed execution
Reproducible experiments and event-based infrastructure
Simulation observability and lower simulation cost
New simulation products assembled from powerful platform configurations rather than custom engineering projects
Apply for this role.We read every application ourselves.
Aetherya is a frontier cognitive simulation lab building computational models of human perception, behavior and decision-making.
Our research focuses on synthetic humans: persistent, behaviorally grounded agents capable of interpreting information, experiencing uncertainty, forming objections, changing beliefs and making decisions within simulated environments.
