Principal Applied Scientist- AI
Core
Lead R&D of frontier AI systems combining foundation models, multimodal reasoning, and agentic workflows to build autonomous enterprise software.
Role type
Principal Applied Scientist (AI Research & Production)
Builds
Autonomous enterprise AI agents, large-scale foundation models, and production ML systems
Domain
Enterprise Automation, Generative AI, Multimodal Systems
Deliverable
production ML models
Required skills
Foundation model training, LLM post-training, Reinforcement Learning from Human Feedback (RLHF), Preference optimization, Reward modeling, Synthetic data generation, Agentic reasoning, Tool use, Function calling, Long-horizon planning, Multimodal learning, Retrieval-Augmented Generation (RAG), Distributed training and inference, Large-scale PyTorch systems, Model alignment and safety
Preferred skills
PhD in CS/ML/AI, End-to-end ML system ownership, Strong Python/PyTorch skills, Technical leadership/publication record
Technologies
PyTorch, JAX, CUDA, vLLM, Ray, DeepSpeed, FSDA, Triton, LangGraph, Semantic Kernel, MCP, Vector databases, Kubernetes, Azure AI, Hugging Face, Distributed GPU training infrastructure
Responsibilities
Lead R&D of large-scale foundation models and agentic AI systems, Design novel approaches for post-training and model alignment, Develop scalable evaluation frameworks, Build production-quality AI systems, Drive innovations in long-context reasoning and multi-agent orchestration, Design large-scale experiments and analyze model behavior, Collaborate with engineering to transition research to production, Mentor scientists and engineers, Influence long-term AI strategy
Seniority
Principal, individual contributor leadership
