Machine Learning Jobs at Adobe with Visa Sponsorship
Machine Learning jobs at Adobe span creative AI, content intelligence, and generative model research, building products used by millions. Adobe has a consistent track record of sponsoring international talent across major visa categories, making it one of the more accessible Technology & Software employers for ML-focused candidates.
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Get Access To All JobsINTRODUCTION
Adobe Journey Optimizer (AJO) powers personalized, real-time customer experiences at massive scale for global brands. Our Reliability Engineering & Operational Intelligence (REOI) team is building AJO's autonomous operating system — an AI-native platform that proactively improves product quality, accelerates issue resolution, and enhances customer experience through intelligent automation and continuous learning.
We are seeking a Machine Learning Engineer who is eager to apply ML and AI to solve real challenges in reliability, quality, and operational intelligence at scale. In this role, you will build AI systems that make AJO progressively more reliable and self-healing — learning from every incident, preventing recurring failures, and ensuring exceptional customer experiences while enabling the platform to scale 4x without scaling operational overhead.
This is a unique opportunity to work at the intersection of production systems, AI/ML, and product quality — where your work directly impacts how millions of customer journeys are delivered reliably every day.
ROLE AND RESPONSIBILITIES
Build AI-powered systems that improve the quality, reliability, and customer experience of AJO — by automating issue detection and resolution with human-in-the-loop approval, learning from operational patterns to prevent recurring failures, and providing real-time visibility into customer health and platform stability.
Develop intelligent knowledge systems that compound expertise over time — using vector embeddings, similarity retrieval, and pattern clustering to ensure every incident investigation builds on past learnings, making the platform progressively smarter and more self-healing.
Design and implement LLM-based workflows using prompt engineering, structured outputs, tool calling, and agentic reasoning patterns to create autonomous capabilities that operate safely at production scale.
Build evaluation frameworks to measure AI system performance: quality improvement rates, automation success rates, mean time to resolution (MTTR) reduction, and customer impact metrics.
Integrate AI capabilities with production infrastructure: Kubernetes, Prometheus, Splunk, GitHub, and 30+ operational data sources — creating closed-loop systems that detect, learn, and act autonomously.
Apply ML techniques to operational data: anomaly detection for early issue detection, time-series forecasting for capacity planning, pattern clustering for recurring failure identification, and predictive analysis for proactive prevention.
Collaborate with SREs, software engineers, and product teams to understand quality and reliability challenges, then design and deploy AI solutions that address them systematically.
Contribute to code reviews, testing, documentation, and CI/CD pipelines — building production-grade ML systems with the same rigor as mission-critical infrastructure.
BASIC QUALIFICATIONS
BS/MS in Computer Science, Machine Learning, Data Science, or related field, with 2-4 years of professional experience (or strong academic/internship experience in ML/AI applied to real-world problems).
Hands-on experience with Python and ML frameworks: scikit-learn, PyTorch, TensorFlow, HuggingFace, or LangChain.
Practical knowledge of LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and prompt engineering techniques for building agentic workflows.
Understanding of vector databases and similarity search (FAISS, Pinecone, ChromaDB, MongoDB Atlas Vector Search, or similar).
Foundational knowledge of ML concepts: embeddings, clustering, classification, evaluation metrics (precision/recall/F1), and model deployment best practices.
Comfortable building APIs and integrating ML models into backend services using FastAPI, Flask, or similar frameworks.
Eagerness to learn production ML operations: model monitoring, A/B testing, continuous evaluation, and safety guardrails for AI systems.
Strong problem-solving skills, attention to detail, and the ability to iterate quickly based on data and feedback.
Excellent communication and collaboration — able to explain ML concepts to non-ML engineers and translate business requirements into technical solutions.
Bonus: Experience with Kubernetes, observability tools (Prometheus, Grafana, Datadog), incident management systems, or building AI agents for operational use cases.
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.
COMPENSATION
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 $102,400 - $202,250 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 $139,700 - $202,250.
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.
EEO STATEMENT
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 accommodations@adobe.com 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.
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.
INTRODUCTION
Adobe Journey Optimizer (AJO) powers personalized, real-time customer experiences at massive scale for global brands. Our Reliability Engineering & Operational Intelligence (REOI) team is building AJO's autonomous operating system — an AI-native platform that proactively improves product quality, accelerates issue resolution, and enhances customer experience through intelligent automation and continuous learning.
We are seeking a Machine Learning Engineer who is eager to apply ML and AI to solve real challenges in reliability, quality, and operational intelligence at scale. In this role, you will build AI systems that make AJO progressively more reliable and self-healing — learning from every incident, preventing recurring failures, and ensuring exceptional customer experiences while enabling the platform to scale 4x without scaling operational overhead.
This is a unique opportunity to work at the intersection of production systems, AI/ML, and product quality — where your work directly impacts how millions of customer journeys are delivered reliably every day.
ROLE AND RESPONSIBILITIES
Build AI-powered systems that improve the quality, reliability, and customer experience of AJO — by automating issue detection and resolution with human-in-the-loop approval, learning from operational patterns to prevent recurring failures, and providing real-time visibility into customer health and platform stability.
Develop intelligent knowledge systems that compound expertise over time — using vector embeddings, similarity retrieval, and pattern clustering to ensure every incident investigation builds on past learnings, making the platform progressively smarter and more self-healing.
Design and implement LLM-based workflows using prompt engineering, structured outputs, tool calling, and agentic reasoning patterns to create autonomous capabilities that operate safely at production scale.
Build evaluation frameworks to measure AI system performance: quality improvement rates, automation success rates, mean time to resolution (MTTR) reduction, and customer impact metrics.
Integrate AI capabilities with production infrastructure: Kubernetes, Prometheus, Splunk, GitHub, and 30+ operational data sources — creating closed-loop systems that detect, learn, and act autonomously.
Apply ML techniques to operational data: anomaly detection for early issue detection, time-series forecasting for capacity planning, pattern clustering for recurring failure identification, and predictive analysis for proactive prevention.
Collaborate with SREs, software engineers, and product teams to understand quality and reliability challenges, then design and deploy AI solutions that address them systematically.
Contribute to code reviews, testing, documentation, and CI/CD pipelines — building production-grade ML systems with the same rigor as mission-critical infrastructure.
BASIC QUALIFICATIONS
BS/MS in Computer Science, Machine Learning, Data Science, or related field, with 2-4 years of professional experience (or strong academic/internship experience in ML/AI applied to real-world problems).
Hands-on experience with Python and ML frameworks: scikit-learn, PyTorch, TensorFlow, HuggingFace, or LangChain.
Practical knowledge of LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and prompt engineering techniques for building agentic workflows.
Understanding of vector databases and similarity search (FAISS, Pinecone, ChromaDB, MongoDB Atlas Vector Search, or similar).
Foundational knowledge of ML concepts: embeddings, clustering, classification, evaluation metrics (precision/recall/F1), and model deployment best practices.
Comfortable building APIs and integrating ML models into backend services using FastAPI, Flask, or similar frameworks.
Eagerness to learn production ML operations: model monitoring, A/B testing, continuous evaluation, and safety guardrails for AI systems.
Strong problem-solving skills, attention to detail, and the ability to iterate quickly based on data and feedback.
Excellent communication and collaboration — able to explain ML concepts to non-ML engineers and translate business requirements into technical solutions.
Bonus: Experience with Kubernetes, observability tools (Prometheus, Grafana, Datadog), incident management systems, or building AI agents for operational use cases.
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.
COMPENSATION
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 $102,400 - $202,250 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 $139,700 - $202,250.
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.
EEO STATEMENT
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 accommodations@adobe.com 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.
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.
See all Machine Learning Jobs at Adobe
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs at Adobe.
Get Access To All JobsTips for Finding Machine Learning Jobs at Adobe
Align Your Research Portfolio to Adobe's Stack
Adobe's ML hiring skews toward generative AI, computer vision, and content understanding. Before applying, audit your portfolio for projects involving diffusion models, multimodal learning, or recommendation systems, since these map directly to Adobe Firefly and Sensei product lines.
Target Teams Actively Publishing Research
Adobe Research regularly publishes at NeurIPS, CVPR, and ICCV. Cross-referencing recent Adobe papers with open roles helps you identify which teams are hiring and tailor your application to their specific research directions rather than applying broadly.
Confirm Sponsorship Intent Before Final Rounds
Ask your recruiter early whether the specific role is approved for H-1B or E-3 sponsorship. Some ML contractor or project-based roles at large tech companies are scoped differently, and confirming sponsorship eligibility before investing in multiple interview rounds avoids late-stage surprises.
Time Your Application Around H-1B Cap Deadlines
If you need H-1B sponsorship, USCIS opens registration in early March for an April 1 fiscal year start. Adobe typically files cap-subject petitions for ML roles during this window, so targeting offers from January through February gives employers enough runway to prepare your petition.
Use Migrate Mate to Filter Adobe ML Roles by Visa Type
Filtering by visa type before applying saves significant time. Migrate Mate lets you browse open Machine Learning roles at Adobe filtered by the visa categories they sponsor, so you can focus only on positions that match your current immigration status.
Prepare Your STEM OPT Extension Documentation Early
If you're on F-1 OPT and your STEM extension is pending, confirm your employer has completed E-Verify enrollment before your OPT expires. Adobe qualifies as a STEM OPT employer, but the E-Verify requirement is your responsibility to verify before your authorization gap becomes an issue.
Frequently Asked Questions
Does Adobe sponsor H-1B visas for Machine Learning roles?
Yes, Adobe sponsors H-1B visas for Machine Learning positions. ML roles at Adobe typically qualify as specialty occupations under USCIS criteria given the advanced degree requirements in fields like computer science, statistics, or electrical engineering. Adobe participates in the annual H-1B cap lottery, so timing your offer and onboarding to align with USCIS registration windows in early March is important.
How do I apply for Machine Learning jobs at Adobe?
Applications go through Adobe's careers portal, but finding roles that match your visa situation requires an extra filter step. Migrate Mate lists open Machine Learning roles at Adobe organized by visa sponsorship type, which lets you target positions that fit your status before investing time in the application process. Tailor your application to Adobe's specific ML product lines like Firefly and Sensei.
Which visa types does Adobe commonly sponsor for Machine Learning positions?
Adobe sponsors H-1B, E-3, TN visa, F-1 OPT, F-1 CPT, J-1 visa, and employment-based Green Card categories including EB-2 and EB-3 for ML roles. Australian citizens have the added advantage of the E-3 visa, which has no lottery and a faster processing timeline than the H-1B. Employees on F-1 STEM OPT can also bridge to H-1B sponsorship while working at Adobe.
What qualifications does Adobe expect for Machine Learning roles?
Most ML roles at Adobe require a master's degree or PhD in computer science, applied mathematics, or a related quantitative field, along with hands-on experience in deep learning frameworks like PyTorch or TensorFlow. Roles tied to Adobe Research additionally expect a publication record at major venues such as CVPR, NeurIPS, or ICCV. Industry roles on Sensei and Firefly prioritize production ML experience over research output.
How do I plan my timeline if I need visa sponsorship for an Adobe ML role?
The critical variable is your current status. If you need a cap-subject H-1B, target an offer by February so Adobe can file during the March registration window, with an October 1 start date. E-3 visa and TN visas process faster and have no lottery. F-1 OPT and STEM OPT bridge periods can cover you while sponsorship is pending, provided E-Verify enrollment is in place.