Adobe is actively hiring for 12 AI and machine learning positions across AI/ML Engineer (8), Research Scientist (3), and AI Product Manager (1) roles. Posted salary ranges span $214K - $384K, with 100% of listings disclosing compensation. The median posted ceiling sits at $265K. Positions are based in San Jose, CA, US, San Francisco, CA, US, Seattle, WA, US. The most frequently requested skills across these postings are Python, Aws, Azure, Rag, Kubernetes. Senior-level roles account for 50% of openings.
Skills & Technologies
Locations
San Jose, CA, US, San Francisco, CA, US, Seattle, WA, US
Hiring by Role Category
Open Positions (12)
Applied Scientist
Senior Manager, Analytics and Data Science - Firefly
Senior Research Scientist
Sr. Applied AI Product Manager
Applied AI Engineer
Research Scientist/Engineer
Senior Agentic AI Engineer
Sr Machine Learning Engineer, Adobe Firefly Services
Machine Learning Engineer
Director, Engineering – Agentic Product
Director of Engineering – Machine Learning & AI Products
Staff Product Designer, AI Foundation
What Adobe's hiring tells you
12 open AI roles across 3 role types puts this company in the scaling phase: past the initial proof of concept, building out a real team. Expect more structure than a startup but less bureaucracy than a major. Good fit for engineers who want ownership without building from zero. Posted compensation range ($214K - $384K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in Research Scientist roles. That is a clue about what Adobe is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the Adobe interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- What problem did the first AI hire solve, and how has scope grown since?
- Where does AI sit in the engineering org, and who owns the budget?
- What is the on-call expectation for AI systems? (If unclear, that means it has not happened yet.)
Adobe AI and ML Hiring
Adobe has 12 active AI and ML roles in our dataset. Open positions span Research Scientist, AI/ML Engineer, AI Product Manager. Compensation ranges from $214K - $384K across disclosed roles. Roles are based in San Jose, CA, US, San Francisco, CA, US, Seattle, WA, US.
Salary Benchmarks
The market median for AI roles is $215,000. Research Scientist roles pay a median of $222,200 across the market. AI/ML Engineer roles pay a median of $214,900 across the market. AI Product Manager roles pay a median of $217,100 across the market. Top-quartile AI compensation starts at $266,300.
Skills Adobe Looks For
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.
AI Role Categories
Research Scientist
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.
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.
Market compensation for Research Scientist roles: $222,200 median across 378 positions with disclosed pay.
AI/ML Engineer
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Market compensation for AI/ML Engineer roles: $214,900 median across 6,420 positions with disclosed pay.
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $217,100 median across 471 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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.
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.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.