Omni模型算法工程师/研究员
Tasks
- Apply reinforcement learning for training
- Build end to end audio to audio models
- Construct multimodal data pipelines
- Convert instructions into action sequences
- Create speech to action models
- Define data quality and sampling strategies
- Deploy models to edge and heterogeneous compute
- Design multimodal model architectures
- Develop embodied agent and task planning systems
- Develop multimodal alignment techniques
- Evaluate model ability safety latency and resource use
- Implement temporal modeling and long context understanding
- Integrate models with robot perception cognition decision and control
- Optimize models with quantization distillation pruning and operator optimization
- Perform instruction tuning and preference alignment
- Train using pretraining and finetuning
- Unify representations across text speech vision video action
Perks/Benefits
- N/A
Skills/Tech-stack
Agent planning | Audio Processing | Audio to Audio | C++ | Context understanding | Data Engineering | Distributed Training | Edge Computing | Embodied AI | End to End | End-to-end learning | Heterogeneous computing | Human Feedback | Instruction Tuning | Knowledge Distillation | Language Models | Large Language Models | Learning from Human Feedback | Linux | Long Context | Long Context Understanding | Model Compression | Model Deployment | Model Evaluation | Model Training | Multimodal Learning | Operator optimization | Preference optimization | Pretraining | Pruning | PyTorch | Python | Quantization | Reinforcement Learning | Reinforcement Learning from Human Feedback | Speech Recognition | Speech to Action | Task planning | Temporal modeling | Transformer | Vision Language Models | Vision-language
Education
Regions
Countries
States
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