Applied Research Engineer - Member of Technical Staff

About the Company

Pilots don’t train with real passengers. Surgeons don’t practice on real people. Yet, the most consequential decisions in society are often pushed straight to production. Simile is changing that. We have built the first AI simulation of society, populated by generative agents based on real humans. Our research pioneered the field of AI-based simulation, proving it is possible to model human behavior with high accuracy. Today, we are developing a Foundation Model to predict human behavior in any situation, at any scale.

We are backed by $100M in funding led by Index Ventures, with participation from Hanabi, A*, Bain Capital Ventures, and AI visionaries including Andrej Karpathy, Fei-Fei Li, Adam D’Angelo, and Guillermo Rauch.

About the Role

As a Member of Technical Staff (MTS) in Research, you will work across the stack to train, evaluate, deploy, and monitor our models of human behavior. At Simile, we maintain a tight research-to-product pipeline. This requires intense scientific rigor; we must be able to trust our experimental methods as they are integrated into production systems that our customers use for making real high-stakes decisions.

We are looking for researchers who find it gratifying to see their work pushed to its absolute limits. You will own the research cycle end-to-end: from designing the initial experiments and validating results to owning the "last-mile" work of deployment.

In this role, you will:

• Extract Insight from Unique Data: Work with massive, proprietary datasets that represent the breadth of human experience, including long-form unstructured interviews, large-scale polls, and passively collected behavioral data.
• Master the Hardware: Write code for the latest NVIDIA chips. You will be responsible for running high-stakes experiments on GPUs and staying at the forefront of modern language model training and fine-tuning methodologies.
• Lead Scientific Discovery: Design rigorous evaluations and conduct experiments that go beyond standard benchmarks to prove the fidelity of our behavioral simulations.
• Push the State-of-the-Art: Stay immersed in the latest developments in simulation research. You will frequently reproduce, critique, and improve upon existing academic papers, maintaining a high standard for academic-quality writing and documentation.
• Own the Lifecycle: Bridge the gap between a research hypothesis and a production-ready model, ensuring that our "flight simulators" for society are grounded in statistical truth.

Requirements

Must Haves

• ML Proficiency: High proficiency in Python and hands-on experience with modern ML frameworks and AI coding tools.
• Experimental Rigor: Experience running experiments on GPUs and a deep understanding of the training/fine-tuning lifecycle for large-scale models.
• Research Literacy: Ability to navigate the frontier of ML research, with the technical skill to reproduce complex papers and the writing skill to document new breakthroughs.
• End-to-End Ownership: A desire to own the full stack of research, from the first line of data processing code to the final deployment in a production environment.

Nice to Haves

• Academic & Technical Foundation: An academic background in Computer Science, Math, Statistics, Deep Learning, Computational Social Science, or a related field.
• Interdisciplinary Expertise: Experience in social science modeling or behavioral economics.
• Large-Scale Systems: Familiarity with distributed training and optimizing inference for multi-agent environments.