Principle Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning
Tasks
- Build Bayesian and Markovian graph chains for uncertainty estimation and probabilistic reasoning
- Define architecture for large scale Bayesian framework based AI systems
- Develop mixture of models and multitask learning architectures
- Develop production machine learning pipelines for batch and real time systems
- Ensure scalability performance and reliability through testing and validation
- Implement model evaluation systems for interpretability hyperparameter optimization and drift detection
- Integrate large language models and multimodal models into Bayesian frameworks
- Lead applied research in Bayesian learning and distributional reinforcement learning
- Provide technical leadership and mentor engineers
Perks/Benefits
- N/A
Skills/Tech-stack
Bayesian Networks | Bayesian Neural Networks | Bayesian learning | C++ | Distributed Systems | Distributional Reinforcement Learning | Ensemble Techniques | External Knowledge Integration | Java | Kafka | Knowledge integration | Language Models | Large Language Models | Large Multimodal Models | Markov Chains | Mixture of Models | Multimodal Models | Multiobjective optimization | Multitask Learning | Neural Networks | Optimization | Probability | PyTorch | Python | Reinforcement Learning | Scala | Sharded Architecture | Spark | Statistics | TensorFlow | Transformers
Education
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