Principal Research Engineer, Post-Training

What You'll Do

Technical Leadership & Mentorship

• 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

Research & Model Development

• 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

Systems & Infrastructure

• 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

Required Qualifications

• 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

Nice to Have

• 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