Principal Engineer – Bayesian, Large Foundational Systems, Distributional Reinforcement Learning
- Job Description:
- Lead groundbreaking applied research in Bayesian systems, distributional reinforcement learning, and multi-modal architectures to drive novel advances in AI and Foundational Intelligence.
- Bridge the gap between theoretical AI/ML advancements and real-world production systems.
- Define and drive the architecture of large-scale Bayesian Framework-based AI systems.
- Develop multi-pass sharded Bayesian + Discriminative/Generative single to multi agent systems for scale and efficiency.
- Build and refine Bayesian or Markovian Graph chains to incorporate uncertainty estimation, adaptive decision-making, and probabilistic reasoning.
- Lead technical direction and strategy for AI/ML systems.
- Requirements:
- Bachelor’s degree in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
- 15+ years of technical experience in Applied Machine Learning, including producing code and deploying production systems.
- Strong programming skills in Python, Scala, Java, or C++, with expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch).
- Proven experience with Bayesian Neural Networks, Bayesian Learning, and Reinforcement Learning.
- Strong math background in probability, statistics, and optimization.
- Experience with building scalable AI/ML systems using technologies like Spark, Kafka, and distributed architectures.
- Familiarity with advanced ML techniques, including Mixture of Models, Ensemble Techniques, multitask learning, and sharded architectures.
- Benefits:
- This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
Apply tot his job
Apply To this Job