Simulation Research Engineer (Robotics AI)

Location: On-site in New York or San Francisco, United States

Compensation: $170,000–$300,000 per year, plus potential equity and benefits.

Company

hirify.global builds data infrastructure for robotics and embodied AI, including data capture, labeling, simulation, and hardware-enabled workflows for training and validating humanoid and embodied AI systems.

What you will do

• Build simulation environments modeling robots, sensors, objects, contacts, materials, dynamics, and real-world scenarios.
• Develop reliable pipelines for parallel rollouts, synthetic data generation, policy training, and evaluation at scale.
• Calibrate simulations against measured data and improve physical and sensing fidelity.
• Apply system identification, domain randomization, and controlled experiments to improve sim-to-real transfer.
• Develop learned dynamics, latent world models, and hybrid physics-learning systems for prediction, planning, and control.
• Turn research prototypes into reusable simulation assets, training infrastructure, and documented methods with researchers and engineers.

Requirements

• Deep experience building and debugging physics-based simulations with MuJoCo, Isaac Sim, or comparable platforms.
• Strong reinforcement learning and embodied AI fundamentals, including observations, actions, rewards, and policy evaluation.
• Experience with learned dynamics, latent world models, or hybrid physics-learning systems.
• Strong Python and C++ systems skills for building scalable and reproducible research tools.
• Ability to design controlled experiments, measure sim-to-real gaps, and trace failures to models, data, policies, or infrastructure.
• Ability to work on-site in New York or San Francisco.

Nice to have

• Experience with simulation or training infrastructure used at scale by a robotics or embodied AI research team.
• Demonstrated sim-to-real transfer in manipulation, locomotion, navigation, or another physical domain.
• Published or open-source work in world models, differentiable simulation, GPU-accelerated simulation, or model-based reinforcement learning.

Culture & Benefits

• Work closely with frontier robotics teams and researchers on real-world data, simulation, learning-based systems, and deployed hardware.
• Compensation is region-specific and determined based on experience, skills, scope, and role level.
• Total rewards may include equity, health and wellness benefits, and other company programs.
• Inclusive hiring practices and interview accommodations are available.