AlignerrCoding & software eval
Robotics ML Expert: MuJoCo & Reinforcement Learning
up to $150/hr
About this role, in Alignerr's words
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 Munich's world-class robotics community 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.
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
Show all 8 itemsHide the other 5 in What you'll do
- 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
Candidate background
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
Show all 6 itemsHide these 6 items in 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
Quoted from selected sections of Alignerr's posting. The full posting may state other conditions. Read the full posting
A closer look
More context, when you need it.
Full job descriptionThe original posting, in Alignerr's words
Posted by Alignerr, reproduced here so you can judge the role before clicking. Original posting ↗
Robotics ML Expert — MuJoCo & Reinforcement Learning
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 Munich's world-class robotics community 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
Working with AlignerrPay, getting started and work, from Alignerr's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Billed weekly, Monday to Sunday, with approved work paid by the following Friday, according to Labelbox's help center. Payment goes through Stripe Connect or PayPal depending on your location, with one provider per country. intercom.help/labelbox/en/articles/13600616-alignerr-paym..., alignerr.com/process
- Before your first paid task
- Sign up, apply, verify your identity, set up billing and onboard. Some jobs add a Zara AI interview or domain assessments. alignerr.com/process
- How much work
- Time to your first project depends on demand and your skills, and there is no minimum number of hours. alignerr.com/faqs
- Contract
- Independent contractor, under terms with ALIGNERR LLC. alignerr.com/faqs, alignerr.com/privacy
Read on the pages linked above on 24 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
How does the pay compare?Across Coding & software eval roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from Alignerr's posting. How we count.
Pay across the field: Coding & software eval
Across 393 roles in this field with a stated hourly rate in US dollars, on 14 platforms, the middle half pays $65 to $120 an hour at the top of the range, and the median is $95. This role's top rate, $150, is in the top quarter. Of the 141 distinct rates we count, 122 are lower and 11 are higher.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| Alignerr (this platform) | 136 | $50 to $400 · median $100 |
| micro1 | 46 | $35 to $350 · median $110 |
| Handshake AI | 43 | $47 to $125 · median $80 |
| Meridial (Invisible) | 34 | $22 to $150 · median $60 |
| Mercor | 33 | $60 to $200 · median $105 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 27 Aug 2026, and still on Alignerr's list when we last read it on 13 Oct 2026.
- The listed pay has not changed since then.
- Posted by Alignerr
- First seen by AIPayList
- Last seen live
- Listing ID
- 85507b74-97f7-48e6-95ba-58e1324549b7
Pay as the source states it: “$100-150/hr”.
Platforms sometimes change a rate after posting. Every listing here is re-read at least once a day and corrected when its source moves, so a difference you see on the platform is usually a change made since the last read. How freshness works.
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