Principal Engineer -In Bayesian, Large Foundational Systems, and Distributional Reinforcement Learning
Tasks
- Build multi agent systems for scale and efficiency
- Conduct code reviews mentor engineers and champion best practices
- Design large scale Bayesian framework based AI architectures
- Develop foundational models with Bayesian techniques and classical machine learning
- Develop uncertainty estimation and probabilistic decision making
- Ensure system reliability through testing and validation
- Implement scalable batch and real time machine learning pipelines
- Integrate LLM and multimodal models with probabilistic reasoning
- Lead applied research in Bayesian learning and distributional reinforcement learning
- Lead technical direction and strategy for AI ML systems
- Perform model evaluation with interpretability hyperparameter optimization and drift detection
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Kafka | Bayesian Neural Networks | Bayesian learning | C++ | Distributed Computing | Drift Detection | Ensemble learning | Hyperparameter Optimization | Java | Language Models | Large Language Models | Machine Learning | Machine Learning Pipelines | Mixture of Models | Multimodal Models | Multiobjective optimization | Multitask Learning | Neural Networks | Optimization | Probability | PyTorch | Python | Reinforcement Learning | Scala | Spark | Statistics | TensorFlow | Transformers | Uncertainty Estimation
Education
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