Research Engineer/Scientist - Human Alignment, Consumer Devices

Responsibilities

• Develop RLHF and post-training methods for multimodal models.
• Build reward models and preference-learning pipelines for adaptive, personalized model behavior.
• Design datasets, rubrics, and evaluation frameworks that capture user preferences, contextual appropriateness, and long-term value in realistic tasks.
• Run experiments on policy improvement using explicit feedback, implicit signals, and model-based grading.
• Work on long-horizon evaluation problems, where model quality depends on behavior improving outcomes over time.
• Collaborate closely with safety researchers to ensure adaptation and personalization remain aligned, interpretable, and bounded by clear constraints.
• Prototype and iterate quickly on training recipes, reward formulations, data pipelines, and evaluation suites for product-relevant behaviors.
• Help define how success is measured for personalized AI systems including trust, appropriateness, and long-term user benefit.

Requirements

• Strong background in machine learning research, with experience in RLHF, reward modeling, preference optimization, or post-training for large models.
• Experience in one or more of: reinforcement learning, ranking, recommender systems, personalization, memory, or human-in-the-loop evaluation.
• Care about rigorous empirical work and know how to design clean experiments, reliable evals, and decision-useful metrics.
• Experience building datasets or eval pipelines grounded in human preferences, rubrics, or real-world product behavior.
• Comfortable working across the stack, from data generation and labeling strategy to training runs, reward functions, and analysis.
• Interest in multimodal AI and how models can learn from richer interaction signals over time.