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About This Role
DESCRIPTION
Humorphic Labs builds AI systems that work as teammates rather than tools. We ship them six months or more ahead of anyone else. The Lab is small on purpose, so it can change direction in a day.
Humorphism concerns the quality of the working relationship between a person and an AI system. It asks whether the system acts proactively, adapts to the person and the situation, earns trust, manages attention, and strengthens human judgment. This role turns those behaviors into testable questions, then answers them with working systems.
You will own the scientific agenda. You will convert a fuzzy behavioral goal into an end-to-end plan that covers data, agent architecture, evaluation, and the product experiment that tests it. You will write code every week and build large parts of the experimental stack yourself, because it does not exist yet.
The first focus areas are agentic products where a domain expert holds judgment the system cannot replace. Amazon Connect places an assistant beside a person handling a live conversation. AWS Bio Discovery places one beside a scientist running experiments. Both need evaluation that measures collaboration quality rather than task completion alone.
The role changes shape as the Lab matures. It starts as hands-on science in close partnership with product and engineering. Later it moves inside a product team to carry adoption of what the Lab proved.
Key job 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.
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.
About the team
The Lab is a lead who still writes code, an Applied Scientist, and engineers. Applied AI Solutions leadership approved it with one instruction, to explore the limits of what we can build, and one number, to ship six or more months ahead of anyone else.
You own the agenda, the hypotheses, and the evaluation. The engineers build the systems that test them, then the products that carry the ones that survive. Neither seat hands work over a wall.
We have a sunset clause. Once a capability is proven and integrated into its product, the Lab moves to the next frontier.BASIC 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
PREFERRED QUALIFICATIONS
- 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
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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
Salary Context
This $198K-$269K range is above the 75th percentile for Research Scientist roles in our dataset (median: $197K across 82 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 9,653 AI roles we're tracking, Research Scientist positions make up 2% of the market. At Amazon Web Services, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 708 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($233K) sits 5% above the category median. Disclosed range: $198K to $269K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 79 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, Research Engineer. Positions span New York, NY, US, Bellevue, WA, US, Atlanta, GA, US. Compensation range: $97K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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