Robotics ML Expert MuJoCo Environments Remote Contract Role

Alignerr is hiring experienced candidates for the role of Robotics ML Expert — MuJoCo Environments. This is a fully remote, flexible hourly contract opportunity for professionals with strong hands-on experience in robotics simulation, reinforcement learning, MuJoCo, robot control, and machine learning workflows.

This role is not a beginner-level opening. It is suitable for candidates who have already worked with physics simulation, reinforcement learning pipelines, MuJoCo environments, MJCF files, reward functions, and robotic task design. Candidates who enjoy building simulation environments and training intelligent agents for real-world robotic behavior can consider this opportunity.

This page explains the job details, responsibilities, required skills, role expectations, preparation tips, and important points candidates should check before applying.

Job Overview

About the Role

The Robotics ML Expert role is focused on designing, building, and improving MuJoCo-based simulation environments for robotics research and AI training. The selected candidates may work on environments that help intelligent agents learn locomotion, manipulation, coordination, and other robotics-related tasks.

In simple terms, this role involves creating realistic simulation setups where AI agents can learn how to move, control robotic systems, interact with objects, and improve performance through reinforcement learning. Candidates may work on reward functions, observation spaces, action spaces, contact dynamics, actuator behavior, and policy evaluation.

This role is suitable for candidates who already understand robotics simulation and machine learning at a practical level. It is especially relevant for people who have worked with MuJoCo, dm_control, Gymnasium, reinforcement learning algorithms, and robot control systems.

Key Responsibilities

• Design, develop, and improve MuJoCo simulation environments for robotics research and AI training.
• Build simulation tasks related to locomotion, manipulation, coordination, and embodied AI behavior.
• Implement and tune reinforcement learning algorithms such as PPO, SAC, TD3, or similar methods.
• Define reward functions, observation spaces, and action spaces for robotic tasks.
• Debug and optimize physics simulations, including contact models, actuator dynamics, and scene configurations.
• Evaluate trained policies for stability, generalization, and possible sim-to-real transfer.
• Document environment specifications, training procedures, experiments, and results clearly.
• Collaborate asynchronously with research teams and align simulation work with project goals.
• Stay updated with robotics learning, embodied AI, simulation methods, and reinforcement learning research.

Required Skills

Candidates applying for this role should have strong hands-on experience, not just theoretical knowledge. The role requires practical comfort with robotics simulation, RL training, and technical debugging.

• Hands-on experience with MuJoCo
• Experience with dm_control, Gymnasium, Gymnasium-Robotics, or similar wrappers
• Strong understanding of reinforcement learning theory and practical training pipelines
• Experience with algorithms such as PPO, SAC, TD3, or related RL methods
• Strong Python programming skills
• Comfort with ML frameworks such as PyTorch or JAX
• Experience designing and tuning reward functions for robotic tasks
• Understanding of robot kinematics, dynamics, and control fundamentals
• Ability to read and write MJCF or XML model files
• Strong technical documentation and written communication skills

Nice-to-Have Skills

• Experience with sim-to-real transfer methods such as domain randomization or system identification
• Familiarity with physics simulators such as Isaac Gym, PyBullet, Drake, or Genesis
• Background in multi-agent environments or hierarchical reinforcement learning
• 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, machine learning, computer science, or a related field

Why This Role Can Be Good for Experienced Candidates

This role can be a strong fit for robotics and ML professionals who want to work on practical simulation environments used for AI training. Instead of only building models, candidates get to shape how intelligent agents learn physical behavior inside simulation.

The role can provide exposure to advanced robotics learning problems such as locomotion, dexterous manipulation, multi-agent coordination, reward shaping, policy stability, and sim-to-real readiness. Candidates who enjoy deep technical work and independent problem-solving may find this opportunity valuable.

Who Should Apply

• Experienced candidates with hands-on MuJoCo experience
• Robotics ML practitioners comfortable with reinforcement learning pipelines
• Candidates who have worked with MJCF/XML model files
• Candidates experienced in reward design and policy evaluation
• Candidates comfortable with Python, PyTorch, JAX, or similar ML tools
• Candidates who can work independently in a remote asynchronous setup
• Candidates interested in embodied AI, robot learning, and simulation environments

Who Should Avoid

• Freshers with no robotics simulation or ML project experience
• Candidates who only have basic Python knowledge
• Candidates who have not worked with MuJoCo or similar physics simulators
• Candidates not comfortable with reinforcement learning concepts
• Candidates who do not like debugging physics simulation issues
• Candidates who prefer highly supervised work instead of independent contract work

What to Prepare Before Applying

• Updated resume focused on robotics and ML experience
• MuJoCo projects or simulation environment examples
• RL training experiments using PPO, SAC, TD3, or similar algorithms
• GitHub repositories or technical portfolio if available
• Examples of reward function design and debugging
• Experience with MJCF/XML model files
• Clear explanation of robotics simulation projects
• Research papers, open-source work, or technical write-ups if available

Candidates should avoid applying with a generic ML resume. This role needs proof of practical robotics simulation work. Mention specific environments built, algorithms used, training challenges solved, and tools used in real projects.

Resume Tips for This Role

For this role, your resume should show hands-on robotics simulation and reinforcement learning experience. Focus on practical work rather than broad claims.

• Mention MuJoCo experience clearly in your skills and project sections.
• Add details about environments you built or modified.
• Include RL algorithms used, such as PPO, SAC, TD3, or model-based methods.
• Highlight reward design, observation/action space design, and policy evaluation experience.
• Add GitHub, papers, demos, or open-source contributions if available.
• Keep your technical claims honest and interview-ready.

Selection Process

The selection process is not clearly specified here. Candidates should check the official application page for the exact hiring steps.

• Application review
• Portfolio or project review
• Technical discussion on MuJoCo and reinforcement learning
• Simulation or task-based evaluation if required
• Final contract discussion

The actual process may vary depending on the organization’s requirements and project needs.

How to Apply

Candidates should apply only through the official application source. Before submitting the application, verify the organization name, role title, contract type, hourly commitment, eligibility, compensation details, and application instructions.

Click Here to Apply

Important Note

Fresher Job Finder is not the hiring organization. We do not guarantee selection, interview calls, contract confirmation, payment, or offer letters. This page is for informational purposes only. Job details can change at any time. Always verify final details on the official application page before applying.

Frequently Asked Questions

Is Robotics ML Expert MuJoCo Environments suitable for freshers?

No. This role is more suitable for experienced candidates with hands-on MuJoCo, robotics simulation, reinforcement learning, and robot control experience.

What are the most important skills for this role?

MuJoCo, reinforcement learning, Python, PyTorch or JAX, reward function design, MJCF/XML modeling, robot control, and simulation debugging are important skills for this role.

Is this a remote job?

Yes. The role is described as fully remote and flexible, with a contract commitment of 10–40 hours per week.

Is this a full-time permanent job?

No. This is listed as an hourly contract role. Candidates should check the official source for contract terms and payment details.

Should I pay money to apply?

No. Candidates should not pay money for applications, interviews, contracts, or offer letters. Apply only through official sources.