Principal Research Engineer, Post-Training
Lead technical vision and execution for post-training systems that transform foundation models into intelligent, engaging products. Drive alignment algorithms, RL, and infrastructure for large-scale LLM training and serving.
About the job
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
Skills
Transformers, Reinforcement Learning, LLMs, Alignment Methods, Gpu-Based Systems, Distributed Training, Kubernetes, Docker, Python, A/B Testing
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