Robotics ML Expert — AI Simulation & MuJoCo

About The Role
What if your expertise in robotics and machine learning could directly shape how the next generation of intelligent agents learn to move, manipulate, and interact with the physical world? We're looking for Robotics ML Experts in Bangalore's thriving AI ecosystem with hands-on MuJoCo experience to design, build, and refine simulation environments that train AI systems to perform real-world tasks — from locomotion and dexterous manipulation to complex multi-agent coordination.
This is a fully remote, flexible contract role for experienced practitioners who live and breathe physics simulation, reinforcement learning, and robot control. If you've spent time wrangling MJCF files, tuning reward functions, and debugging contact dynamics, this role was made for you.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design, develop, and iterate on MuJoCo simulation environments for robotics research and AI training
Implement and tune reinforcement learning algorithms (PPO, SAC, TD3, etc.) to train agents in simulated tasks
Define reward functions, observation spaces, and action spaces that produce robust, transferable policies
Debug and optimize physics simulations — contact models, actuator dynamics, and scene configurations
Evaluate trained policies for stability, generalization, and sim-to-real transfer potential
Document environment specifications, training procedures, and experimental results clearly and thoroughly
Collaborate asynchronously with research teams to align simulation work with broader project goals
Stay current with the latest advances in robot learning, simulation, and embodied AI
Who You Are
Strong hands-on experience with MuJoCo (or MuJoCo via dm_control, Gymnasium/Gymnasium-Robotics, or similar wrappers)
Solid understanding of reinforcement learning theory and practical training pipelines
Proficient in Python and comfortable with ML frameworks such as PyTorch or JAX
Experienced in defining and shaping reward functions for complex robotic tasks
Familiar with robot kinematics, dynamics, and control fundamentals
Able to read and write MJCF/XML model files and understand their physics implications
Self-directed, detail-oriented, and comfortable working independently in an async environment
Strong written communicator who can document technical work clearly
Nice to Have
Experience with sim-to-real transfer techniques (domain randomization, system identification)
Familiarity with other physics simulators — Isaac Gym, PyBullet, Drake, or Genesis
Background in multi-agent environments or hierarchical RL
Published research or open-source contributions in robotics, RL, or embodied AI
Experience with imitation learning, model-based RL, or world models
Graduate-level coursework or degree in robotics, ML, computer science, or a related field
Why Join Us
Work on cutting-edge robotics and AI simulation projects alongside leading research labs
Fully remote and flexible — work when and where it suits you
Freelance autonomy with the structure of meaningful, milestone-driven work
Directly influence how AI agents learn to interact with the physical world
Engage with a global community of top-tier ML and robotics practitioners
Potential for ongoing work and contract extension as new projects launch

Work arrangement
No