Principal Research Engineer, Post-Training

About the job

Responsibilities

• Define and drive the technical roadmap for mid- and post-training systems, balancing research innovation with production reliability and scalability.
• Mentor and grow a team of researchers and engineers through technical guidance, design reviews, and career development.
• Establish best practices for experimentation, model development, and deployment.
• Lead the development of alignment algorithms, optimization techniques, and training objectives to improve model capabilities and data efficiency.
• Drive advances in mid- and post-training methodologies including reinforcement learning, preference optimization, supervised fine-tuning, and emerging alignment approaches.
• Identify and execute high-impact research opportunities that improve model behavior, safety, and user engagement.
• Develop robust evaluation frameworks and quality signals to measure real-world model performance.
• Lead the design of efficient training and inference systems for large-scale generative models.
• Architect scalable data pipelines that transform diverse data sources into high-quality training datasets.
• Partner with infrastructure teams to optimize distributed training, GPU utilization, and serving efficiency.
• Drive improvements in experimentation platforms, data quality systems, and model observability.

Requirements

• PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent industry experience.
• Significant experience leading technical projects or teams in machine learning, AI research, or large-scale distributed systems.
• Experience scaling and mentoring high-performing research and engineering teams.
• Deep understanding of modern machine learning techniques, including transformers, reinforcement learning, alignment methods, and large language models.
• Strong track record of delivering impactful research or applied ML systems in production environments.
• Expertise in designing, building, and maintaining production-quality ML systems and infrastructure.
• Experience training, serving, debugging, and optimizing large-scale models on GPU-based systems.
• Experience leading teams working on large language model training, mid-training, or post-training.
• Experience with product experimentation, online evaluation, and A/B testing frameworks.
• Strong software engineering skills with the ability to write clean, maintainable, and scalable code.
• Excellent communication skills and the ability to influence technical direction across teams.
• Lead complex, cross-functional initiatives across data, training infrastructure, evaluation, and model serving.
• Hands-on experience working directly with open-source models like Mistral and Qwen, particularly adapting them via mid- and post-training for specific personas, creative writing, or role-playing applications.
• Familiarity with cloud-native ML infrastructure, including Kubernetes, Docker, and modern orchestration platforms.
• Publications in leading machine learning conferences or demonstrated contributions to the broader AI community.