AI Research Engineer (Multi-Modal Reinforcement Learning)
Tasks
- Analyze model behavior across modalities and identify bottlenecks
- Create simulation environments and datasets for training and benchmarking
- Design and execute evaluation protocols for reproducible experiments
- Design scalable RL infrastructure for distributed training and evaluation
- Develop reward modeling strategies to improve alignment and stability
- Explore next generation RL paradigms for environment feedback
- Publish research in AI conferences
- Research reinforcement learning methods for multimodal systems
Perks/Benefits
- Autonomy
- Fully remote
- Global First Work Environment
- Innovation-driven culture
- Research publication support
Skills/Tech-stack
Deep learning | Distributed Training | Evaluation | Exploration | Machine Learning | Multi-Modal | Multi-modal AI | Optimization | Policy learning | PyTorch | Reinforcement Learning | Reward Modeling | Sample efficiency | Simulation
Education
Roles
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