AI Research Engineer (Agentic Post-training)
Tasks
- Build model benchmarking systems
- Build model diagnostic systems
- Build multi agent coordination
- Collaborate with research, engineering, and product teams
- Conduct end to end research and engineering for agentic AI post training
- Create evaluation frameworks
- Design training pipelines for post training
- Develop training infrastructure
- Identify system bottlenecks
- Implement tool and function calling
- Improve instruction following
- Improve model factuality
- Improve multi step reasoning
- Integrate production feedback into training loops
- Optimize efficiency reliability and performance of tool augmented models
- Perform data curation for training
Perks/Benefits
Skills/Tech-stack
Agent systems | Benchmarking | Data Curation | Diagnostics | Distributed Training | Fine Tuning | Function Calling | GPU Training | Instruction Tuning | Language Models | Large Language Models | Machine Learning | Model Evaluation | Multi-Agent | Multi-Agent Systems | Multi-GPU | Multi-GPU Training | Multi-node | Multi-node Training | Multimodal Learning | PyTorch | Python | Reinforcement Learning | Tool use | Training Infrastructure
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
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