Simile

Simile

Applied Research Engineer - Member of Technical Staff

San Francisco · Staff+

Sponsorship not specified$200k-$400kDetected 126 days ago
PythonFull-Stack DevelopmentMachine LearningDeep LearningNLPStatisticsResearch

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.
  • We are looking for researchers who find it gratifying to see their work pushed to its absolute limits.
  • 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.

Responsibilities

  • Design rigorous evaluations and conduct experiments that go beyond standard benchmarks to prove the fidelity of our behavioral simulations.
  • 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.
  • Our hiring journey is designed to help both sides align on fit, working style, and expectations.
  • 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.
  • Lead Scientific Discovery: Design rigorous evaluations and conduct experiments that go beyond standard benchmarks to prove the fidelity of our behavioral simulations.
  • 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.
  • If you require support or reasonable accommodations during the

Requirements

  • ML Proficiency: High proficiency in Python and hands-on experience with modern ML frameworks and AI coding tools.
  • Interdisciplinary Expertise: Experience in social science modeling or behavioral economics.
  • High proficiency in Python and hands-on experience with modern ML frameworks and AI coding tools.
  • Familiarity with distributed training and optimizing inference for multi-agent environments.

Skills

  • Final offers are based on experience, specialized skills, interview performance, and relevant training.

Compensation

  • $200,000 - $400,000 USD
  • At Simile, we provide competitive compensation packages that include base salary, equity, and comprehensive benefits.

Benefits

  • At Simile, we provide competitive compensation packages that include base salary, equity, and comprehensive benefits.
  • Comprehensive medical, dental, and vision coverage.
  • Flexible time off policies to support work-life balance.
  • Equity: Grants are available for eligible roles, subject to board approval.
  • application process due to a disability, please let us know.

Company info

  • Pilots don't train with real passengers.
  • Actors don't rehearse with real audiences.
  • 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.
  • We are happy to assist.
  • At Simile, we maintain a tight research-to-product pipeline.

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.

This listing is sourced directly from Simile's careers page and normalized into a canonical job model.