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About This Role
The opportunity
Adobe Express enables everyone — individuals and large organizations alike — to produce impressive content effortlessly. The AI Foundations team builds the flexible, scalable AI framework that powers creativity at scale across design, imaging, motion, and personalization.
We are looking for an Engineering Manager to lead and grow a team of engineers building the AI infrastructure for Adobe Express. This role sits at the intersection of technical leadership and people management — you will own the delivery of the AI stack spanning Agentic AI, Imaging AI, Motion AI, and Personalization AI, while developing the engineers who build it. You will partner closely with product, research, and cross\-functional engineering teams to shape the technical direction of one of Adobe’s most strategic platforms.
This is an early\-level people management role (M30\), well suited to someone with a strong engineering foundation who has been leading \& managing teams for 3 –5 years and is looking to deepen their impact through the growth of others and the delivery of high\-consequence systems at scale.
What you'll do
Technical direction and delivery
Own end\-to\-end delivery of AI infrastructure workstreams — including LLM orchestration, inference services, data pipelines, and evaluation frameworks — from planning through production.
Maintain strong enough technical depth to participate in system design reviews, challenge architectural decisions, and unblock your team on complex problems.
Work with your team to set and meet engineering quality standards: observability, fault tolerance, latency guarantees, security, and responsible AI practices — including bias awareness and data privacy for AI systems.
Make deliberate, explicit calls on technical debt versus feature velocity, and hold the team accountable to those decisions.
Develop and communicate a coherent technical roadmap for your area, balancing immediate delivery with sustainable long\-term architecture.
Cross\-functional partnership
Partner with product management \& engineering leadership to decompose high\-level product requirements into concrete technical requirements — breaking ambiguous asks into scoped workstreams with clear dependencies, effort estimates, and sequencing.
Drive prioritization across competing demands — balancing new feature work, infrastructure investment, and reliability improvements with a clear, defensible rationale the team and stakeholders can align on.
Collaborate with AI research, data science, and platform teams to integrate in\-house and third\-party models and APIs into production\-quality serving systems.
Represent your team’s work and direction to senior stakeholders — communicating progress, risks, and technical trade\-offs clearly to both technical and non\-technical audiences.
Team leadership and people development
Manage and grow a team of 6–10 engineers across varying seniority levels, providing regular coaching, feedback, and career development support.
Drive a strong hiring bar — own the full recruiting lifecycle for your team, from sourcing through offer, and help build an inclusive, high\-performing culture.
Foster a collaborative, psychologically safe environment where engineers can do their best work and grow into senior and staff roles.
What you'll bring
3 –5 years of engineering management experience, with a track record of delivering complex infrastructure or platform projects through a team.
A strong technical foundation in distributed systems, AI/ML infrastructure, or large\-scale service development — enough to earn credibility with senior engineers and make sound architectural trade\-offs.
Experience owning team execution end\-to\-end — including structured prioritization across competing workstreams, dependency management, and shipping reliably in an agile, fast\-moving environment. Can articulate trade\-off decisions clearly, not just make them.
Clear, structured communication skills — able to translate technical trade\-offs into business terms for PMs and non\-technical stakeholders, and to influence direction without authority across teams.
Comfort navigating ambiguity: defining scope, making decisions with incomplete information, and adapting plans quickly as systems and priorities evolve.
Working fluency in modern AI/ML concepts — LLM orchestration, inference infrastructure, prompt engineering, AI output evaluation, and data pipelines — sufficient to guide technical decisions, set a quality bar, and grow team capability.
Demonstrated ability to grow engineers: coaching, setting expectations, giving actionable feedback, and supporting career progression at multiple levels.
Preferred qualifications
Masters degree or equivalent experience in Computer Science, Machine Learning, or a related field.
Background as a hands\-on engineer in data infrastructure, ML platform, or large\-scale backend systems before moving into management.
Experience hiring and ramping engineers across a range of seniority levels, including senior and staff engineers.
Exposure to Generative AI development — LLMs, diffusion models, or multimodal systems — either as an individual contributor or as a manager overseeing such work.
Familiarity with MLOps practices: feature stores, model registries, evaluation pipelines, and deployment workflows.
Actively tracks emerging AI/ML trends and has a considered view on what’s applicable versus overhyped — and can bring that perspective into team planning conversations.
Awareness of security, data privacy, and responsible AI concerns specific to AI\-backed systems — including bias, safety, and handling of user data in model pipelines.
Why Adobe
At Adobe, we are crafting the future of creativity through intelligence. The AI Foundations team combines the agility of a startup with the scale and mission of one of the world’s leading software companies. As an Engineering Manager on this team, you will shape the people, systems, and practices that bring adaptive, autonomous, and human\-centered AI to millions of creators worldwide — and develop the next generation of engineering talent along the way.
About Adobe
Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry\-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.
Our 30,000\+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.
Let’s Adobe together
At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture , focus on people, purpose and community , Adobe for All , comprehensive benefits programs , the stories we tell , the customers we serve, and how you can help us advance our mission of empowering everyone to create.
Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.
Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email [email protected] or call \+1 408\-536\-3015\.
AI Use Guidelines for Interviews:
Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.
At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience .
Expected Pay Range: Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $146,300 \-\- $289,900 annually. Pay within this range varies by work location and may also depend on job\-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.
In California, the pay range for this position is $200,200 \- $289,900
At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC \= base \+ commission), and short\-term incentives are in the form of sales commission plans. Non\-sales roles starting salaries are expressed as base salary and short\-term incentives are in the form of the Annual Incentive Plan (AIP).
In addition, certain roles may be eligible for long\-term incentives in the form of a new hire equity award.
State\-Specific Notices:
California :
Fair Chance Ordinances
Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.
Colorado:
Application Window Notice
If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.
Massachusetts:
Massachusetts Legal Notice
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $146K-$289K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
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.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Adobe, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
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.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($218K) sits 18% above the category median. Disclosed range: $146K to $289K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Adobe AI Hiring
Adobe has 15 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager. Positions span San Jose, CA, US, Seattle, WA, US, Lehi, UT, US. Compensation range: $200K - $397K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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