Principal Applied Scientist, Humorphic Labs

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• 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

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave.

Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, TX, Austin – 198,900.00 - 269,000.00 USD annually

• 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.

A day in the life

Your week has two centers of gravity.

Most days you build. You take a claim about how a teammate should behave design the smallest experiment that can falsify it and run it. You read interaction traces from real sessions then argue with the engineers about what they mean.

The rest of the week belongs to the people the work is for. You sit with a contact center agent or a bench scientist and watch where the system helps and where it intrudes. You leave with the next hypothesis. Often you leave with evidence that kills the last one.