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Sr MLOps Engineer, AI Platform Training

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

We're looking for an outstanding, ML Infrastructure Engineer for Adobe’s AI Training Platform, Adobe Firefly. You will be part of a team MLOps engineers closely working with the Engineering teams on building, scaling, and securing the AI Platform. This enables the Firefly product teams to easily manage and deploy Machine Learning capabilities used by Adobe client applications.

The Applied Research groups from Adobe Research and other App Teams in Adobe will deploy thousands of models onto this platform in a variety of lifecycle stages (early research, development, productization, optimization, etc). This platform offers ML model development and training at scale, with high-cost efficiency, and on a wide variety of hardware platforms across multiple clouds.

What You'll Do

  • Identify and implement methodologies and solutions to increase reliability, scalability, security, and efficiency.
  • Ensure the highest uptime and Quality of Service (QoS) for Adobe’s customers through operational excellence.
  • Define service level objectives (SLOs) and indicators (SLIs) to represent and measure service quality.
  • Support and maintain globally distributed, multi-cloud (public and/or private) environments.
  • Automate common, repeatable tasks at a large scale to streamline operational procedures.
  • Identify areas to improve service resiliency through techniques such as chaos engineering, performance/load testing, etc.
  • Coordinate with other Adobe platform teams and service providers (primarily AWS) to innovate on Generative AI as a Service.

What You’ll Need to Succeed

  • You excel in undefined environments and get excited about finding pragmatic solutions to complex technical or organizational challenges.
  • You keep up with the industry trends and grow your knowledge and skills to solve technical problems.
  • Experience in building and scaling distributed systems, as well as experience with containerization and orchestration technologies like Kubernetes.
  • Production level expertise with containerization orchestration engines (e.g. Kubernetes) and proven understanding of modern, continuous development techniques and pipelines (IaC, CI/CD, ArgoCD, Git)
  • Fundamental programming skills, ideally practical experience in one (and preferably more) of the following languages: Python, Go
  • Good knowledge of infrastructure configuration management tools like Ansible and Terraform.
  • Experience in using observability and tracing-related tools like InfluxDB, Prometheus, and Elastic Stack.
  • An understanding of AI/ML, including ML frameworks, public cloud, and commercial AI/ML solutions - familiarity with Pytorch, SageMaker, HuggingFace, NVIDIA TensorRT or OpenAI Triton a plus.
  • A Bachelor's or Master's degree in Computer Science, Electrical Engineering, a related field, or equivalent industry experience.

#FireflyGenAI

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 12:01 AM 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.

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 $153,600 -- $286,600 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 will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.

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.
 

Adobe aims to make Adobe.com 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 (408) 536-3015.

Adobe values a free and open marketplace for all employees and has policies in place to ensure that we do not enter into illegal agreements with other companies to not recruit or hire each other’s employees.

Average salary estimate

$220100 / YEARLY (est.)
min
max
$153600K
$286600K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Sr MLOps Engineer, AI Platform Training, Adobe

Join Adobe as a Sr MLOps Engineer for our innovative AI Platform Training team in beautiful San Jose, California! At Adobe, we’re all about changing the world through extraordinary digital experiences. Picture yourself collaborating with talented engineers and researchers to build, scale, and secure Adobe Firefly, our cutting-edge AI Training Platform. In this role, you will help streamline Machine Learning deployments for Adobe's client applications, ensuring high reliability, security, and efficiency across multi-cloud environments. Your days will be dynamic, and you'll be instrumental in defining and achieving service level objectives to keep our services top-notch. If you’re someone who thrives in challenging environments and embraces the latest industry trends, you’ll find a fun yet professional atmosphere here. From automating operational tasks to improving service resilience through advanced techniques—your expertise in containerization, Kubernetes, and observability tools will be key to success. Bring your passion for AI/ML and your programming skills, and let’s empower creators together. If this sounds like the perfect opportunity for you, we want to hear from you!

Frequently Asked Questions (FAQs) for Sr MLOps Engineer, AI Platform Training Role at Adobe
What are the responsibilities of a Sr MLOps Engineer at Adobe?

As a Sr MLOps Engineer at Adobe, your responsibilities will include building and maintaining the AI Training Platform known as Adobe Firefly. You will work closely with engineering teams to enhance model lifecycle management, ensuring reliability, scalability, and security across multi-cloud environments. You'll also define service level objectives that measure service quality and automate operational tasks to streamline processes.

Join Rise to see the full answer
What qualifications do I need to be a Sr MLOps Engineer at Adobe?

To succeed as a Sr MLOps Engineer at Adobe, you should have a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field. It's essential to demonstrate expertise in building and scaling distributed systems and working with containerization technologies like Kubernetes. Familiarity with AI/ML frameworks and programming languages such as Python or Go will also be highly beneficial.

Join Rise to see the full answer
What skills are important for a successful Sr MLOps Engineer at Adobe?

Key skills for a Sr MLOps Engineer at Adobe include proficiency in container orchestration, continuous development techniques, and infrastructure configuration management tools like Ansible or Terraform. Experience with observability tools like Prometheus and a solid understanding of AI/ML principles will enhance your ability to contribute effectively to the team.

Join Rise to see the full answer
What kind of environment can I expect as a Sr MLOps Engineer at Adobe?

You can expect a dynamic and inclusive environment at Adobe as a Sr MLOps Engineer. We value innovation and collaboration, allowing new ideas from every corner of the organization to flourish. Our commitment to employee experiences ensures you will be respected and supported while working on exciting, cutting-edge projects.

Join Rise to see the full answer
What is the salary range for a Sr MLOps Engineer at Adobe?

The salary range for a Sr MLOps Engineer at Adobe varies depending on location and experience, with annual compensation between $153,600 and $286,600. Your recruiter can provide more specifics tailored to your qualifications during the hiring process.

Join Rise to see the full answer
Common Interview Questions for Sr MLOps Engineer, AI Platform Training
Can you explain your experience with Kubernetes in relation to your role as a Sr MLOps Engineer?

Detail your hands-on experience with Kubernetes, focusing on how you have utilized it for deploying and managing containerized applications effectively. Share examples of scaling applications, resolving deployment issues, and leveraging Kubernetes features to enhance cloud infrastructure reliability.

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How do you ensure the reliability and scalability of machine learning models in production?

Discuss your methodologies for monitoring models post-deployment, including performance metrics you track and any automation tools you use. Provide insights on handling increases in load and optimizing resources to maintain service quality.

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What steps do you take to automate operational procedures in an AI/ML context?

Explain the tools and scripting languages you have used to automate repetitive tasks. Provide examples of specific automations you've developed to improve efficiency, such as CI/CD pipelines or infrastructure provisioning.

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Describe a challenging technical problem you’ve faced as an MLOps engineer and how you solved it.

Use this opportunity to showcase your problem-solving skills. Describe the context, the technical challenges you encountered, and the innovative solutions you derived to overcome these issues, emphasizing teamwork and collaboration.

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What observability tools have you used in your previous roles and how did they impact your work?

Share your experience with observability tools such as Prometheus or Elastic Stack, emphasizing how they helped in monitoring system health, troubleshooting issues, and improving the overall performance of machine learning systems.

Join Rise to see the full answer
How do you keep up with the latest trends and advancements in Machine Learning?

Discuss your methods for staying informed, such as following industry publications, attending conferences, and participating in online courses. Share how this knowledge has influenced your work in MLOps.

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Can you explain how you would set service level objectives (SLOs) and indicators (SLIs) for an AI platform?

Outline the process you would take to define measurable and meaningful SLOs and SLIs. Include considerations around customer impact, operational metrics, and common pitfalls to avoid in monitoring service quality.

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What role does security play in your approach as a Sr MLOps Engineer?

Emphasize the importance of security in MLOps, detailing how you ensure that machine learning pipelines are secure by design. Discuss any tools or frameworks you utilize for implementing best security practices.

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What strategies have you employed for chaos engineering in AI/ML systems?

Describe your approach to chaos engineering, including methods for testing system resilience through failure simulations. Elaborate on how these strategies inform future design and operational practices.

Join Rise to see the full answer
How do you handle collaboration with cross-functional teams in your MLOps projects?

Offer examples of successful collaborations you've had, highlighting the importance of clear communication, understanding different team dynamics, and finding common goals to work towards in MLOps projects.

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DATE POSTED
March 31, 2025

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