Senior AI/ML Fraud Engineer Full-time

Our Company

Changing the world through digital experiences is what Adobe’s all about. We give everyone-from emerging artists to global brands-everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.

We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

The Opportunity

As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities. These activities include bot attacks, account takeover (ATO) incidents, phishing schemes, theft, and product misuse.
What you’ll Do

In this role, you will tap into your expertise in computational analysis, engineering, and algorithm design to identify and thwart patterns of fraud and abuse. You will be engaged in researching the latest advancements in AI, ML, DL, NLP, LLMs, and multi-agent systems to enhance the robustness of our fraud detection capabilities. Your contributions will be pivotal in developing real-time solutions that protect our users and uphold the integrity of our platform. Collaborating closely with other data scientists, security experts, and software engineers, you will play a key role in fostering a secure digital environment. This position is a unique opportunity to employ ground breaking technologies to address the challenges of online fraud.
What you need to succeed

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• 5+ years of experience in Data Science, with substantial experience in AI/ML engineering.
• A minimum of 3 years of experience in developing AI, ML, DL, NLP, LLM, and/or multi-agent algorithms, with a focus on fraud detection and prevention.
• Proven track record of successful implementation of AI-based strategies, including LLMs and multi-agent systems, to combat online fraud.
• In-depth understanding of online or software fraud activities, including bots, ATOs, phishing, theft, and misuse.
• Proficiency in programming languages such as Python, R, Java, or similar, with an emphasis on engineering principles.
• Exceptional problem-solving skills and the ability to think critically and innovatively.
• Excellent communication and teamwork skills for effective multi-functional collaboration.
• Commitment to staying updated with the latest research and trends in AI, ML, DL, NLP, cybersecurity, LLMs, and multi-agent systems.
• Experience with big data technologies and tools (e.g., Databricks, Hadoop, Spark) is a plus.

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 $127,300 — $229,300 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.

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.

Adobe is proud to be an Equal Employment Opportunity and affirmative action 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 applicable characteristics protected by law. Learn more.

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