Principal Applied Scientist (AI)

Location: Bellevue, United States

Company

hirify.global creates enterprise automation software designed to transform how organizations work.

What you will do

• Lead research and development of large-scale foundation models and agentic AI systems for enterprise automation.
• Design post-training, model alignment, reinforcement learning, preference optimization, reward modeling, and synthetic data approaches.
• Build evaluation frameworks for reasoning, tool use, planning, and autonomous task completion.
• Develop production-quality AI systems combining LLMs, multimodal models, retrieval, planning, memory, and external tools.
• Run large-scale experiments, analyze model behavior, and improve systems through data-driven evaluation.
• Mentor scientists and engineers, collaborate with engineering and product teams, and influence long-term AI strategy.

Requirements

• Deep expertise in several areas including foundation model training, LLM post-training, RLHF, preference optimization, reward modeling, synthetic data, model evaluation, agentic reasoning, tool use, planning, multimodal learning, RAG, distributed training, and model alignment.
• Significant experience building and deploying production AI systems based on large language or multimodal foundation models.
• End-to-end ownership of machine learning systems from research and experimentation through production deployment.
• Strong programming skills in Python and deep experience with PyTorch or similar machine learning frameworks.
• Experience training or fine-tuning large models with distributed compute infrastructure.
• PhD or equivalent research experience in computer science, machine learning, artificial intelligence, statistics, robotics, or a related discipline.

Nice to have

• Strong publication record or demonstrated technical leadership in applied AI.
• Experience with JAX, CUDA, vLLM, Ray, DeepSpeed, FSDP, Triton, LangGraph, Semantic Kernel, MCP, vector databases, Kubernetes, Azure AI, Hugging Face, or distributed GPU training infrastructure.

Culture & Benefits

• Work at the intersection of applied research and production engineering.
• Many roles allow flexibility in when and where work gets done, depending on business and role requirements.
• Inclusive workplace with equal opportunities and reasonable accommodations available on request.
• Applications are assessed on a rolling basis, with no fixed deadline.