Head of Simulation Engineering (Expression of Interest)

We are meeting senior leaders for this planned function as we fundraise. This is an exploratory conversation, not an immediately funded salaried vacancy or a guaranteed start date. No cash salary is available today; any equity arrangement must be agreed separately. You are not being asked to begin work by expressing interest.

Use the Apply form below to introduce yourself and indicate your interest. We can discuss mutual fit, scope, funding, compensation, and timing before either side makes a commitment.

Turn simulation research into an experimental engine that can be inspected, reproduced, and improved. Lead the engineering function at the boundary between scientific methods and dependable software.

Subconscious is building the intelligence to run the economy. We start with a simulator for business decisions, using causal experiments on simulated markets to test products, pricing, messaging, and go-to-market choices before capital is committed. The long-term ambition is to help businesses discover unmet demand and build around what people actually need.

Own simulation implementation, experiment execution semantics, reproducibility standards, and the simulation engineering hiring plan. Partner with causal research on method design, evaluation on quality, and infrastructure on reliable operation.

This is a hands-on leadership role. Set the functional roadmap and propose staffing and spending priorities with the founding team. Hiring and budget commitments depend on approved resources; an established team or funded budget is not assumed.

Use these as an initial agenda, refined with the founders against the product, customer needs, and resources at your start.

• Trace an experiment from configuration to output and document which inputs, model versions, random states, and artifacts are required to reproduce it.

• Establish an implementation contract for treatments, populations, execution, and result aggregation, with meaningful correctness checks.

• Profile a representative workload and improve its largest quality, throughput, or cost constraint without silently changing the experimental question.

• Build a research-to-production path with versioned interfaces, reviewable changes, and explicit failure handling.

Research ideas become reusable capabilities with explainable behavior. Track reproducibility, correctness under independently specified test cases, experiment throughput, and cost. Make changes in model behavior distinguishable from execution defects.

• You have led research engineering, simulation, scientific computing, or ML systems work and shipped robust implementations of complex methods.

• You can reason about stochastic systems, numerical behavior, experiment design, and performance while writing and reviewing production code.

• You can lead a technical roadmap across researchers and engineers and make explicit tradeoffs between exploration and stability.

We are looking for evidence of judgment, execution, and leadership. A particular company pedigree or previous executive title is not a substitute for that evidence.

We are actively fundraising. Only equity-based compensation is currently available; no cash salary is available today. The equity grant and its terms have not been set for this role and must be agreed before joining. Any future salary, incentive plan, benefits, hiring budget, or paid start date depends on financing and written agreement. Funding and its timing are not guaranteed.

Before deciding to join, discuss the grant structure, fully diluted ownership basis, vesting, dilution, applicable exercise terms, company funding position, and working arrangement. A company valuation is not the cash value of an equity grant.

Remote · United States. The planned role is full time; timing is to be agreed. Expect direct collaboration with the founders, work across scientific and commercial boundaries, and responsibility for doing the early work yourself. The current role does not include visa sponsorship. Confirm practical collaboration hours and any travel expectations during the initial conversation.

Describe a research method you brought into production. What could go wrong in the translation, how did you test correctness, and what did you change after real use?

Please do not share confidential employer or customer materials. Bring your questions about our product, evidence, team, finances, and the authority attached to the role. Mutual diligence matters for a decision of this size.