ML Systems - Member of Technical Staff

About the Company Simile is The Simulation Company. We simulate human behavior to keep people at the center of the decisions that shape the world. With AI, anyone can create a product, a campaign, a policy, or a script — the bottleneck has moved upstream. The hard question is no longer whether you can create something, but what to create, for whom, and how to bring it to life. Those are fundamentally human decisions, and they shouldn't be left to chance or handed off to an algorithm. We're building the infrastructure to understand human behavior at scale and to represent humans in an increasingly agentic world. Our mission is to simulate all eight billion people on earth. We launched five months ago. Since then we've grown revenue 5x, built a new foundation model for human behavior that has run tens of millions of simulations for F100 enterprises, trained a first-of-its-kind confidence model that predicts the accuracy of every simulation, and released the first product that lets organizations verifiably predict the future. The world's leading companies use Simile to make business-critical decisions — from consumer leaders like CVS Health and Wealthfront to professional services organizations like Deloitte and Gallup — strategizing product launches, entering new markets, and forecasting earnings calls. We've raised over $200M at a $2B post-money valuation led by Greenoaks, wi [... source excerpt omitted ...] the model lifecycle runs on: data ingestion and schema design, distributed training, evaluation, serving, and monitoring. We are the reason a researcher's hypothesis can become a production simulation in days rather than quarters. Two things make this problem unusual. First, our research-to-product pipeline is unusually tight - the experimental methods we validate on Monday are integrated into systems customers use to make high-stakes decisions. Second, simulating a society means running inference over populations of agents, not single requests. A single customer study can mean millions of model calls with interdependent state. Cost per simulation and latency per agent are not ba [... source excerpt omitted ...] Technical Staff in ML Systems, you will build the platform our researchers train, evaluate, and deploy on - and own it through the last mile, where a trained checkpoint becomes a production service serving millions of interdependent agent calls at a cost per simulation we can afford. This is a role for someone who is energized by both halves of that. You will spend some weeks designing the data schemas and training pipelines a research team depends on, others profiling a serving path to find where the FLOPs and GPU memory are going, and others still bringing up cluster nodes or deleting the third redundant copy of a code path. The common thread is leverage: every improvement you