Interested in this Research Scientist role at Agentio?
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About Agentio
Agentio builds AI-native infrastructure for creator advertising. Our platform uses AI to turn creative and performance signals into campaign strategies, match brands with creators, recommend sponsorship prices, and review videos against brand requirements.
Each campaign generates feedback: how brands respond to our recommendations, which offers creators accept, and how the resulting content performs. We build self improving loops that learn from these behaviors and outcomes to better understand preferences, predict performance, and improve campaign decisions.
The work sits between research and product. You'll formulate research problems from product needs, test and extend methods from academia and industry, develop new approaches when existing methods fall short, and work with engineering and product to translate successful research into production systems.
What You'll Work On
Multimodal intelligence. Build systems that understand creators, brands, briefs, and content across video, audio, image, and text.
Ranking, recommendation, and prediction. Develop models that predict creator-brand fit and campaign outcomes and make better decisions from sparse, heterogeneous feedback.
Foundation models and agents. Apply LLMs and multimodal models to campaign planning, discovery, creative understanding, execution, and optimization. Use Agentio's proprietary data for prompting, retrieval, fine-tuning, post-training, and, where it makes sense, specialized model training.
Learning and decision-making. Turn campaign data into training signals, evals, and learning loops while tackling problems in optimization, exploration, measurement, and marketplace dynamics.
Part of the role is deciding which advances in AI and ML are useful for Agentio and where new methods are needed.
What You'll Do
- Own research problems from problem formulation through experimentation, validation, and product application.
- Design rigorous experiments and evaluations, develop new methods where needed, and establish evidence that research advances improve product and marketplace outcomes.
- Develop models, algorithms, training approaches, datasets, and evaluation methods when existing techniques are insufficient. Partner with engineering to turn successful research into reliable product capabilities.
- Help define Agentio's research agenda, identify high-value research questions, and set a high bar for experimental rigor across applied ML work.
Who We're Looking For
We're looking for a researcher with strong technical depth who wants their work to change a production product, not stop at a paper or prototype.
- PhD in machine learning, AI, computer science, statistics, or a related field, with a track record of original ML research demonstrated through peer-reviewed publications or comparable research contributions.
- Depth in one or more areas such as foundation models, multimodal learning, recommendation and ranking, representation learning, reinforcement learning and decision-making, causal inference, optimization, or adjacent fields.
- Experience applying research to real products or production systems, including turning ambiguous product problems into research questions and translating successful results into measurable product improvements.
- Strong implementation skills: you can prototype your own ideas, work directly with large datasets and modern ML stacks, and collaborate closely with engineers on productionization.
- Strong knowledge of current AI research and the judgment to distinguish technically interesting work from approaches that will matter in practice.
What You'll Get
- The opportunity to build a first-of-its kind business as an early team-member and make a meaningful impact in the way brands share their stories and creators live off their work.
- Crash-course in what it takes to scale a start-up with first-hand exposure to the different foundational business drivers and needs.
- The chance to work with an exceptional team and help build our engineering culture
- A collaborative, transparent, and engaging work environment.
Benefits & Perks
- Flexible PTO – Take the time you need to recharge
- Comprehensive Health Coverage – Top medical plan through Aetna
- Dental & Vision Plans – Protect your smile and your sight with coverage from Guardian
- 401(k) Retirement Plan – Invest in your future while you build your career
- $100 Monthly Lifestyle Credit – Use it to shop with our amazing Agentio Brand Partners
- Daily lunches and snacks in the office to keep you at your best
- Dinner provided Monday - Thursday
- Fully subsidized gold tier access on Wellhub (Includes LifeTime, Barry's, Solidcore, Headspace, etc)
- Gym in the building! – Your workout, right downstairs
Agentio is an equal-opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
Compensation Range: $195K - $400K
Salary Context
This $195K-$400K 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 Agentio, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($297K) sits 34% above the category median. Disclosed range: $195K to $400K.
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.
Agentio AI Hiring
Agentio has 2 open AI roles right now. They're hiring across Research Engineer, Research Scientist. Positions span New York, NY, New York, NY, US. Compensation range: $400K - $400K.
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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