Lead Applied Scientist, Human-AI Collaboration

Amazon Inc.; Seattle (WA); On-site; USD 199,000 - 269,000; Full time

Job summary

Amazon Austin is seeking a senior AI scientist to own the scientific agenda for human‑AI collaboration in agentic and multimodal systems. You will translate behavioral goals into end‑to‑end experiments and write code weekly to build large parts of the experimental stack.

You will partner with product and engineering to evaluate collaboration quality, trust, adaptation, and recovery, shaping products that ship six months ahead of competitors.

Qualifications

• PhD in computer science, machine learning, artificial intelligence, or a related technical field, or a Master's degree with equivalent applied science experience, or an equivalent body of work
• Experience developing agentic AI systems or large language model applications through an end-to-end product cycle
• Experience designing evaluation for systems that have no standard benchmark
• Experience writing substantial research or production code in Python or a comparable language
• Experience leading complex scientific work across engineering and product partners
• Evidence of independent decisions in ambiguous, consequential technical environments
• Experience with post-training methods, including supervised fine-tuning and reinforcement learning
• Experience with multimodal models across language, speech, and vision
• Experience with speech systems or real-time conversational systems
• Experience measuring collaboration quality, trust, adaptation, or human contribution in AI systems
• Experience building agent systems that use memory, tools, planning, and recovery mechanisms
• Experience founding a scientific program, or joining an early team before that team had established its methods
• A record of scientific influence through publications, patents, open-source work, or deployed systems
• Experience mentoring senior scientists and engineers without moving away from hands-on work
• Experience partnering with design, product, and behavioral research from problem definition through product validation
• Experience taking a proven method into a product team and staying until that team adopted it

Responsibilities

• Own the scientific strategy for human-AI collaboration across agentic and multimodal systems.
• Convert desired interaction behaviors into falsifiable hypotheses, evaluation tasks, and system requirements.
• Build the evaluation system that measures teammate behavior, trust, adaptation, human contribution, and failure recovery.
• Design agent systems that use memory, tools, planning, and recovery, then test them with the people who do the work.
• Design data collection and curation for language, speech, and interaction traces.
• Build significant parts of the experimental stack yourself, because that stack does not exist yet.
• Diagnose failures across data, models, orchestration, evaluation, and product interaction.
• Define the requirements engineering needs to turn a proven method into a product capability.
• Partner with design, product, engineering, and behavioral research from problem definition through product validation.
• Run experiments with partner product teams, then report what worked, what failed, and what changed as a result.
• Set the standard for reproducible experiments, evidence, and scientific review inside the Lab.
• Mentor scientists and engineers without moving away from hands‑on work.
• Represent the work in internal reviews and in appropriate external scientific venues.

Skills

PhD in CS/ML/AI

End-to-end agentic AI

Evaluation design

Python coding

Leadership across teams

Independent decision making

Education

PhD in CS/ML/AI or related

Master's with applied science exp

Tools

Python

Job description

Amazon Austin is seeking a senior AI scientist to own the scientific agenda for human‑AI collaboration in agentic and multimodal systems. You will translate behavioral goals into end‑to‑end experiments and write code weekly to build large parts of the experimental stack.

You will partner with product and engineering to evaluate collaboration quality, trust, adaptation, and recovery, shaping products that ship six months ahead of competitors.