About the job
Embrace the role of a Machine Learning Engineer and help build the next generation of AI-powered creative platforms. Work with cutting-edge technologies to develop scalable AI infrastructure, microservices, and data pipelines. Collaborate with talented engineers and researchers to deliver impactful, production-ready AI solutions that power innovation across design, imaging, and personalization.
Responsibilities
Contribute to the development of core platform components that support AI experiences in Adobe Express.
Build and improve backend services, microservices, and workflows that connect models, APIs, data systems, and product features.
Help develop data and inference pipelines for training, evaluation, fine-tuning, and deployment of ML models.
Support runtime systems for inference and orchestration with attention to reliability, observability, and performance.
Work on storage, caching, and data-access patterns to improve efficiency, scalability, and cost.
Collaborate with engineers, researchers, and product teams to deliver production-ready AI capabilities.
Participate in debugging, testing, monitoring, and operational improvements for AI platform services
Qualifications
Minimum
3+ years of experience in software engineering, backend infrastructure, data systems, ML infrastructure, or related areas.
Good understanding of distributed systems fundamentals, backend services, and scalable system design.
Experience building or supporting APIs, data pipelines, or event-driven systems.
Proficiency in Python, Java, C++, or Go.
Familiarity with cloud environments, service deployment, and production engineering practices.
Strong problem-solving skills and the ability to work well in a collaborative team environment.
Clear communication skills and willingness to learn from cross-functional partners.
Preferred
Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or a related technical field.
Experience with technologies such as Kafka, Spark, Flink, or similar distributed data frameworks.
Exposure to generative AI systems such as LLMs, multimodal models, or diffusion models.
Familiarity with MLOps concepts such as experiment tracking, model deployment, or evaluation workflows.
Interest in agentic AI concepts such as tool use, task planning, or memory systems.