Computer Vision Researcher (Simulation and Embodied AI)

Department: Software

Location: New York

Compensation: $150K – $250K • Offers Equity

Employment Type: FullTime

Computer Vision Researcher (Simulation & Embodied AI)

About Mecka AI

Mecka AI is building the data infrastructure layer for robotics and embodied AI.

We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies. Our datasets power models that learn from the physical world — enabling robots to understand, reason, and act in real environments.

As robotics systems evolve, combining real-world data with simulation-driven learning is critical to unlocking robust, generalizable behavior.

The Role

We’re hiring a Computer Vision Researcher with a focus on simulation-driven learning for robotics.

This role sits at the intersection of vision, simulation, and control. You’ll work on using real-world data to inform simulated environments, and apply techniques like reinforcement learning and contact modeling to improve motion — particularly for hands, manipulation, and lower-body movement.

You’ll work closely with data, engineering, and customer teams to bridge the gap between captured data → simulation → deployable behavior.

Responsibilities

Simulation & Learning Systems

• Build and iterate on simulation environments for robotic learning

• Use real-world datasets to inform and improve simulated environments

• Apply reinforcement learning (RL) to learn contact-rich behaviors and motion policies

• Focus on improving dexterous manipulation and lower-body motion

Vision & Data Integration

• Develop pipelines that translate captured video and sensor data into usable simulation inputs

• Work on perception systems that support simulation fidelity (pose, state estimation, object understanding)

• Align real-world data distributions with simulation environments

Contact Modeling & Motion

• Model physical interactions (contact, force, constraints) in simulation

• Improve smoothness, stability, and realism of learned motion

• Help bridge sim-to-real gaps for manipulation and locomotion

Experimentation & Evaluation

• Design experiments to evaluate model performance in simulation and real-world settings

• Analyze failure modes and iterate on data, models, and environments

• Work with customers to validate whether data + simulation outputs meet their needs

Cross-Functional Collaboration

• Work closely with:

  • Data teams (capture + labeling pipelines)

  • Engineering teams (infrastructure + deployment)

  • External customers (robotics / AI labs)

• Translate research ideas into practical, usable systems

Who You Are

Required Experience

• MSc or PhD in robotics, computer vision, machine learning, or a related field

• Strong experience with simulation environments (e.g., Isaac Gym, MuJoCo, or similar)

• Experience applying reinforcement learning to control or robotics problems

• Strong programming skills in Python (C++ is a plus)

• Solid understanding of vision, state estimation, and/or perception systems

Strong Signals

• Experience working on dexterous manipulation, hands, or locomotion

• Experience modeling contact-rich interactions in simulation

• Experience working on sim-to-real transfer

• Experience with vision-language-action (VLA) or multimodal systems

• Experience working with large-scale real-world datasets

You Are

• Deeply curious about how robots learn and move

• Comfortable working across research and engineering boundaries

• Able to move from idea → experiment → iteration quickly

• Excited by messy, real-world problems — not just clean benchmarks

• Motivated to build systems that actually get used

Why This Role

• Work on core problems in simulation-driven robotics learning

• Help define how real-world data and simulation interact at scale

• Partner with leading AI labs and robotics companies

• High ownership and direct impact on product and research direction

• Opportunity to push forward how robots learn manipulation and movement