Member of Technical Staff

Palo Alto

Full time

On-site

Engineering

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.

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.

• Architect Foundation Data Schemas: Lead a significant conceptual and engineering rewrite of our data architecture. You will redefine the schema across survey databases, API DBs, and training files to transform our system.

• Optimize Computational Performance: Perform algorithmic refactoring of internal data loading and ingestion pipelines. You will identify and resolve performance bottlenecks and correctness concerns, ensuring that high-throughput data streams are optimized for large-scale model training.

• Master the Hardware & Infrastructure: Architect and manage the end-to-end infrastructure. You will write high-performance code for the latest NVIDIA chips, overseeing continuous data ingestion, GPU-based training workflows, and the deployment of production-ready models.

• Engineer Scientific Evaluations: Design and build high-priority evaluation tooling that goes beyond standard benchmarks. You will develop rigorous statistical frameworks to prove the fidelity and accuracy of our simulations.

• Push the State-of-the-Art: Stay at the frontier of simulation research by reproducing, critiquing, and improving upon academic papers. You will translate theoretical breakthroughs into production-ready improvements, maintaining a high standard for technical documentation.

• Own the Lifecycle: Exercise independent judgment to bridge the gap between a research hypothesis and a deployed system. You will own the full research-to-production pipeline, ensuring our simulations are grounded in statistical truth and production-level reliability.

Must Haves

• Academic & Technical Foundation: Requires a Master’s Degree in Computer Science, Mathematics, Statistics, Deep Learning, or a related quantitative field.

• Systems & ML Proficiency: High proficiency in Python and deep hands-on experience with modern ML frameworks (e.g., PyTorch, JAX). You must demonstrate the ability to refactor complex codebases for performance and architectural integrity.

• Inference & Deployment Expertise: Proven experience owning the deployment pipeline for large-scale models. You understand the training/fine-tuning lifecycle and can architect the infrastructure required for continuous data ingestion and model monitoring.

• Research & Data Literacy: Ability to navigate the ML research frontier and reproduce complex papers. You must possess the technical skill to tackle "messy" data states and the writing skill to document breakthroughs with academic-level rigor.

• End-to-End Technical Ownership: A demonstrated track record of owning the full stack of research engineering—from designing initial experiments and data schemas to the "last-mile" work of production deployment and optimization.

Nice to Haves

• Interdisciplinary Expertise: Experience in social science modeling or behavioral economics.

• Large-Scale Systems: Familiarity with distributed training and optimizing inference for multi-agent environments.

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

• Salary Range: $200,000 – $400,000 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.

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.

Equal Opportunity: Simile is an equal opportunity workplace. We welcome applicants of all backgrounds and identities, valuing an environment where everyone can contribute authentically.

Accommodations: If you require support or reasonable accommodations during the application process due to a disability, please let us know. We are happy to assist.