Applied Research Engineer

As an Applied Research Engineer, you'll design and build advanced systems to collect, analyze, and optimize human-in-the-loop data for training cutting-edge AI models. Your work will focus on techniques such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), and novel feedback mechanisms to ensure that frontier models align with human values and preferences.

This is a unique opportunity to blend research, engineering, and human-centered design to shape the next generation of AI systems.

What Youll Do

• Develop state-of-the-art methods for aligning AI systems with human intent using techniques like RLHF and beyond.
• Design systems to rigorously measure and improve the quality of human feedback used in AI training.
• Build tools to enhance data labeling processes through AI-assisted workflows, active learning, and adaptive sampling.
• Investigate the impact of different types of feedbacke.g., demonstrations, critiques, comparisonson model performance and behavior.
• Create algorithms to optimize how AI learns from humans, improving adaptability and safety.
• Translate research breakthroughs into practical, scalable tools that integrate directly into production workflows.
• Publish and present your work at top-tier ML/AI venues and actively engage with the broader AI research community.
• Help define best practices and contribute to the evolution of industry standards in human-AI alignment.

What You Bring

• Ph.D. or Masters in Computer Science, Machine Learning, AI, or related field.
• 3+ years of experience solving complex ML problems with real-world impact.
• Deep knowledge of frontier model training, data-centric AI, and alignment techniques.
• Strong expertise in building systems for human data quality measurement and optimization.
• Proficiency in Python and frameworks such as PyTorch, JAX, or TensorFlow.
• A publication record at top-tier conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.).
• Ability to rapidly prototype, test, and iterate research ideas into working systems.
• Excellent analytical thinking, problem-solving skills, and a strong bias toward action.
• Comfortable collaborating across multidisciplinary teams and clearly communicating complex ideas.

Were committed to redefining what it means for AI to learn from humans. Our research spans machine learning, human-computer interaction, and AI ethicsensuring real-world applicability, transparency, and responsibility in every system we build. Youll join a team that values curiosity, rigor, and a deep passion for pushing the boundaries of whats possible in AI.