IFMMachine Learning Engineer — Reinforcement Learning
AI in this role
As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.
The Role
We’re looking for an RL infrastructure engineer to help extend and scale our end-to-end RL training systems. You’ll work side-by-side with world-class researchers and engineers to:
- Extend distributed training frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)
- Integrate rollout generation, reward computation, trajectory processing, policy updates, and weight synchronization
- Build robust config + launch systems across multi-node, multi-GPU clusters
- Own experiment tracking, metrics logging, and job monitoring for external visibility
- Improve training system reliability, maintainability, and performance
Hands-on experience developing end-to-end RL infrastructure is required. Strong infrastructure and systems experience is what we value most.
How we rate this
Machine Learning Engineer — Reinforcement Learning at IFM rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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