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.

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