Principal Applied Scientist- AI
Tasks
- Build production quality AI systems using LLMs multimodal models retrieval and external tool use
- Collaborate with engineering and product teams to transition research into production systems
- Design and run large scale experiments to analyze model behavior and iterate using evaluation data
- Design post training approaches for model alignment and reinforcement learning
- Develop scalable evaluation frameworks for reasoning tool use and planning
- Drive innovations in long context reasoning workflow generation and multi agent orchestration
- Influence long term AI strategy through technical leadership and scientific innovation
- Lead research and development of foundation models and agentic AI systems for enterprise automation
- Mentor scientists and engineers and raise technical standards
Perks/Benefits
- N/A
Skills/Tech-stack
Azure AI | CUDA | DPO | Data Generation | DeepSpeed | Distributed GPU | Distributed GPU training | Distributed Training | FSDP | Foundation Model | Foundation Model training | Function Calling | GPU Training | Hugging Face | Human Feedback | JAX | Kubernetes | Langgraph | Language Models | Large Language Models | Learning from Human Feedback | Long Horizon Planning | Model Alignment | Model Evaluation | Model Training | Multimodal Learning | PPO | Preference optimization | PyTorch | Python | RAG | Ray | Reinforcement Learning | Reinforcement Learning from Human Feedback | Retrieval-Augmented Generation | Reward Modeling | Synthetic Data Generation | Synthetic data | Tool use | Triton | VLLM | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Engineering | Master of Science | PhD
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