JOB LEVEL
P50
EMPLOYEE ROLE
Individual Contributor
We are seeking an experienced Staff Software Development Engineer with deep expertise in cloud infrastructure and a passion for building scalable, production-grade ML systems. As part of the Applied Research and Technology Services organization, you will play a meaningful role crafting the operational backbone for high-performance, reliable, and globally scaled machine learning services. In this role, you’ll work closely with multi-functional collaborators. These include Adobe Research, Adobe AI Platforms, and product engineering teams. Together, you will architect solutions that speed up innovation and improve service resilience. You will also provide technical leadership and define, document, and enforce guidelines adopted across teams. You will own technical direction for core service infrastructure and MLOps, influence architectural decisions across multiple teams, and raise the operational maturity of the organization through standards, reusable platforms, and mentorship. You will evaluate and introduce new infrastructure, optimization, and agentic technologies with clear value and adoption plans. This position is ideal for someone who thrives at the intersection of DevOps, MLOps, systems engineering, and automation.
Key Responsibilities
Build and automate cloud infrastructure provisioning, scaling, and deployments using industry-standard tools and infrastructure-as-code practices.
Architect and implement end-to-end MLOps pipelines for packaging, deploying, and monitoring large-scale ML services.
Build and integrate telemetry agents to capture operational, performance, and inference metrics across distributed ML services.
Build backend dashboards and observability workflows that surface quality, performance, traffic, and reliability insights for ML services.
Lead the development of Agentic Ops solutions to optimize large-scale ML production workflows, reduce MTTR, and increase service engineering productivity.
Develop and maintain robust CI/CD pipelines (e.g., GitLab CI, GitHub Actions, Jenkins) enabling automated model conversion, optimization (PTQ/QAT), and artifact packaging.
Drive standards in reliability, cost optimization, and operational readiness across service deployments.
Qualifications
8+ years of experience in DevOps, SRE, or cloud infrastructure engineering roles
Demonstrated experience designing and managing MLOps lifecycles , including model deployment, inference optimization, and production monitoring.
Strong knowledge of CI/CD methodologies and tools such as GitOps , Docker, Terraform, GitHub Actions, GitLab CI, or Jenkins.
Hands-on expertise with Kubernetes orchestration , including frameworks such as Kubeflow, Argo Workflows, or similar systems.
Strong programming skills in Python , with experience building automation tooling for ML or DevOps workflows.
Proficiency with observability and monitoring platforms (e.g., Prometheus, Grafana, Splunk, New Relic) for building reliable production systems.
Experience optimizing distributed architectures for cost efficiency, reliability, and performance .
Familiarity with deep learning frameworks (e.g., PyTorch , TensorFlow ) and model optimization tools such as ONNX, TensorRT , TFLite , AOT , etc., is a strong plus.
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 positionis $159,200 -- $301,600 annually. Paywithin this range varies by work locationand 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 $208,300 - $301,600
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
Feb 23 2026 12:00 AM
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.
Internal Opportunities
Creativity, curiosity, and constant learning are celebrated aspects of your career growth journey. We’re glad that you’re pursuing a new opportunity at Adobe!
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At Adobe, you will be immersed in an exceptional work environment that is recognized around the world. You will also be surrounded by colleagues who are committed to helping each other grow through our unique Check-In approach where ongoing feedback flows freely. If you’re looking to make an impact, Adobe's the place for you. Discover what our employees are saying about their career experiences on the Adobe Life blog and explore the meaningful benefits we offer.
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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.
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