Research Scientist - Multimodal Agent, Consumer Devices

Details

The Future of Computing Research team is an applied research team within the Consumer Devices group focused on developing methods, models, and evaluation frameworks for the future of computing. The team works at the frontier of multimodal AI, turning emerging model capabilities into useful, trustworthy product experiences.

The role focuses on RLHF and post-training for personalized, multimodal AI systems. The work includes building learning and evaluation foundations for models that become more context-aware, adaptive, and useful over time, including reward modeling, preference learning, long-horizon evaluation, and policy improvement for systems operating in realistic user settings.

This position is based in San Francisco, California, and follows a hybrid work model with three days in the office per week.

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 policy-improvement experiments using explicit feedback, implicit signals, and model-based grading.
• Work on long-horizon evaluation problems where model quality depends on whether behavior improves outcomes over time.
• Collaborate with safety researchers to ensure that adaptation and personalization remain aligned, interpretable, and bounded by clear constraints.
• Prototype and iterate on training recipes, reward formulations, data pipelines, and evaluation suites for product-relevant behaviors.
• Help define measures of success for personalized AI systems, including trust, appropriateness, and long-term user benefit.
• Collaborate with engineers, designers, product teams, and safety researchers to turn research into real systems.

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.
• Ability to design clean experiments, reliable evaluations, and decision-useful metrics.
• Interest in training models against nuanced behavioral objectives.
• Experience building datasets or evaluation pipelines grounded in human preferences, rubrics, or real-world product behavior.
• Comfort 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.
• Interest in product-shaping research involving trust, alignment, and long-term user value.

Benefits

• Base salary of $380,000–$445,000 per year.
• Equity and performance-related bonuses for eligible employees.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for health, dependent care, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, company holidays, and paid office closures.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.