Machine Learning Engineer Jobs at Adobe with Visa Sponsorship
Machine Learning Engineer jobs at Adobe sit at the intersection of research and production systems, spanning recommendation engines, generative AI, and creative intelligence tools. Adobe has a well-established track record of sponsoring work visas for ML engineers and supports multiple pathways, from OPT to long-term permanent residence.
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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 Engineer Jobs at Adobe
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Adobe.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Adobe
Align your portfolio with Adobe's AI products
Adobe hires ML engineers to build systems behind Firefly, Sensei, and content intelligence features. Tailor your projects and GitHub portfolio to show experience in generative models, recommendation systems, or multimodal learning before you apply.
Target roles with explicit sponsorship language
Not every Adobe ML posting explicitly lists visa sponsorship eligibility. Filter for roles that confirm H-1B or E-3 support in the job description, which signals the hiring team has already cleared the position through internal headcount and legal review.
Start OPT early to create filing buffer
If you're on F-1 OPT, Adobe typically files your H-1B cap petition in March for an October 1 start. Apply to roles at least six months before your OPT expires so there's time to interview, receive an offer, and complete the LCA process with DOL.
Prepare documentation for specialty occupation evidence
USCIS scrutinizes ML engineer petitions for specialty occupation status. Compile your transcripts, degree equivalency evaluations, and a clear breakdown of how your role requires a specific technical degree, not just general computer science knowledge.
Use Migrate Mate to find open ML roles at Adobe
Adobe posts ML engineer openings across research, applied science, and product engineering tracks, and they move quickly. Use Migrate Mate to filter Adobe job listings by visa type so you only see roles where sponsorship has been confirmed for your status.
Negotiate offer timing around PERM filing windows
If you're targeting a Green Card through Adobe's EB-2 or EB-3 track, ask your recruiter about the typical PERM initiation timeline after hire. Starting PERM earlier matters significantly if you're from India or China given the priority date backlog.
Frequently Asked Questions
Does Adobe sponsor H-1B visas for Machine Learning Engineers?
Yes, Adobe sponsors H-1B visas for Machine Learning Engineers. Adobe participates in the annual H-1B cap lottery and files petitions each spring for an October 1 start date. ML engineer roles at Adobe typically qualify as specialty occupations under USCIS standards given their degree requirements in computer science, statistics, or a related technical field.
Which visa types are commonly used for Machine Learning Engineer roles at Adobe?
Adobe supports H-1B, E-3 visa (for Australian citizens), TN visa (for Canadian and Mexican nationals), F-1 OPT and CPT, J-1 visa, and employment-based Green Cards including EB-2 and EB-3. For ML engineers already in the U.S. on F-1 OPT, Adobe will typically file an H-1B petition before OPT expires to maintain continuous work authorization.
How do I apply for Machine Learning Engineer jobs at Adobe?
Applications go through Adobe's careers portal, where you can filter by role and location. ML engineer postings at Adobe typically require a portfolio of applied work, so prepare links to published research, production systems, or open-source contributions before applying. You can also use Migrate Mate to browse current Adobe ML openings filtered by visa sponsorship type.
What qualifications does Adobe expect for Machine Learning Engineer roles?
Adobe generally expects a graduate degree in computer science, machine learning, statistics, or a closely related field for ML engineer positions, though strong applied experience can supplement a bachelor's degree on some teams. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow, and familiarity with large-scale model training or deployment, is consistently emphasized across Adobe's ML job descriptions.
How do I plan my visa timeline when joining Adobe as a Machine Learning Engineer?
The H-1B lottery window opens in March each year, and USCIS typically begins accepting registrations in late March for an October 1 start. If you're on OPT, make sure your expiration date gives you enough runway to clear the lottery and have an approved petition before your grace period ends. For cap-exempt pathways like E-3 or TN, processing through the U.S. consulate typically takes two to four weeks, giving you more scheduling flexibility.