Member of Data Staff

About the Team

Every agent in our simulation is grounded in data from a real person. That makes the supply chain that drives data acquisition and first-party collection the raw material of our product. This is what drives the difference between a model that predicts human behavior and one that approximates it.

Data sits upstream of research, engineering, and every customer deployment. We decide which populations we can credibly simulate, which datasets are worth buying, and how faithfully our agents reflect the people they are modeled on. We work in a small, high-ownership team with direct access to the researchers and customers who consume what we build.

About the Role

As a Member of Data Staff, you will own the full picture of how data enters and flows through Simile - both the third-party datasets we license and the first-party data we collect.

On the sourcing side, you will map the frontier of the data landscape and secure the datasets that make our simulations predictive across new domains and geographies. On the collection side, you will run the supply chain that turns data from real people into grounded agents. This includes designing data collection instruments, interacting with vendors and partners, and the quality and representativeness standards that determine whether a simulation can be trusted.

Your core responsibilities will include:

• Expanding our coverage of the world: Deciding which populations Simile should be able to simulate next, then going and getting the data that makes it possible. Much of what you want will not be for sale, which means finding who holds it and showing them our vision for the future.
• Running Simile's data machine: Expanding and running the operations behind our own human data collection - running the supply chain behind Simile's data engine, which includes panel and field vendor management, incentive structures, throughput, and cost per completed participant.
• Finding the richest datasets to improve our simulation of the world: Structuring agreements around how we actually use data - training, fine-tuning, and derivative agent behavior that persists long after a contract term ends. Most data agreements are not written with foundation models in mind, and getting these terms right is the difference between an asset we own and one we license.
• Building our always-on feedback loop: Turning what research and forward deployed teams need into a concrete sourcing and supply chain roadmap - and, just as importantly, tracking which data measurably improved the model so the next round of spend is better informed than the last.
• Defending data fidelity: Owning the question of whether our agents actually resemble the people they are modeled on. You will set the bar for sample composition and response quality, catch fraud and low-effort participants before they reach a model, and hold the line when a dataset is convenient but not credible.
• Trust and compliance: Working with legal so that consent, privacy, and usage rights hold up to the scrutiny of enterprise and government partners. Our access to sensitive populations depends on getting this right the first time.

Requirements
Must Haves

• You are excited about enhancing Simile's data supply chain: You are excited about expanding Simile's global data partnerships and the engine through which we collect data all over the world.
• You are interested in building scalable processes: You can hold many live threads at once and still know the status of each. When you leave a project, the person after you can reconstruct every decision and why it was made.
• You have intuition for what makes an interesting dataset: You know when a dataset is worth spending time with, when an exclusivity clause is worth paying for, and when to keep looking for alternatives. You are willing to say no to something impressive.
• You have negotiating experience or aptitude: You are effective in rooms where you have no leverage and no warm introduction. Some of our highest-value sources will come from convincing an organization to do something it has never done before.
• You are comfortable looking at and thinking about data. You think about things like data biases, sample selection, ideal data schema for the simulation task, the tradeoffs of various collection strategies, and what high quality data should be defined as.

Nice to Haves

• Consulting or investing experience: You have experience tackling complex, ambiguous problems in a fast-paced environment. You are a strong first-principles problem solver.
• Procurement experience: You have negotiated contracts with vendors and managed competing requests and responsibilities.
• Technical fluency: You are comfortable using coding agents to run your own checks and automate your own workflow without waiting on someone else.

Compensation & Benefits

At Simile, we provide competitive compensation packages that include base salary, equity, and comprehensive benefits.

• Salary Range: $150,000 - $300,000 USD
  
  • Note: Final offers are based on experience, specialized skills, interview performance, and relevant training.
• Equity: Grants are available for eligible roles, subject to board approval.
• Health & Wellness: Comprehensive medical, dental, and vision coverage.
• Time Off: Flexible time off policies to support work-life balance.

Our Process

We prioritize thoughtful conversations and clear examples of past work. Our hiring journey is designed to help both sides align on fit, working style, and expectations.

Reapplication Policy: To ensure a fair and thorough evaluation for all applicants, Simile observes a 90-day waiting period before reconsidering candidates for the same role.