Senior Machine Learning Engineer - II

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

The AI Foundation Team at Adobe Express aims to develop a groundbreaking AI stack using internal and external technologies to improve feature speed and quality.

Develop ML models and services, collaborate with teams, improve user experience at Adobe Express.

Experience Requirements:

8 or more years of Proven experience offered or in a related occupation.

As a Machine Learning Engineer & Applied Researcher on the AI Foundation Pillar for Adobe Express, you will develop new ML models, platforms, and services. Your work will cover the entire ML lifecycle, from research to training and inference on multimodal datasets. You will collaborate with product, engineering, and research teams to integrate sophisticated ML capabilities into Adobe Express, improving the user experience with intelligent suggestions, automated content generation, and personalized recommendations. You will also build scalable training pipelines, deploy and supervise inference services, and continuously enhance model performance for a global audience.

More Detailed Duties Include:

• Research, design, and implement advanced ML models and pipelines for training and inference at scale, including techniques in computer vision, NLP, deep learning, and generative AI.
• Integrate Large Language Models (LLMs) and agent-based frameworks to support multimodal creative workflows, enabling rich, context-aware content generation and dynamic user experiences.
• Collaborate with multi-functional teams to translate product requirements into ML solutions, iterating from proof-of-concept to fully productionized services.
• Develop robust platforms for continuous model training, experimentation, A/B testing, and monitoring, ensuring that model quality and relevance remain consistently high.
• Leverage distributed computing technologies and cloud infrastructures to handle large-scale data processing, feature engineering, and real-time inference, optimizing for performance and cost-efficiency.
• Implement reliable APIs and microservices that serve ML models to end users, ensuring alignment to standard methodologies in security, compliance, scalability, and maintainability.
• Stay ahead of with emerging ML research, tools, and frameworks, evaluating and integrating new technologies such as sophisticated LLMs, reinforcement learning-based agents, and innovative inference optimization techniques.
• In the near future, we will expand the model coverage to include more creative tasks. We will also improve the model architectures to achieve better latency and accuracy. Additionally, we will integrate federated learning or domain adaptation strategies to reach diverse audiences.

Special Skills Requirements:

• Proficiency in Python and Java for ML model development and systems integration.
• Hands-on experience with deep learning frameworks, including TensorFlow and PyTorch.
• Expertise in handling distributed training and large-scale inference platforms, including Spark, Hadoop, or Kubernetes.
• Demonstrated experience working with LLMs and agent frameworks to develop advanced AI-based experiences.
• Proficiency in computer vision and NLP techniques for multimodal content understanding and generation.
• Experience in developing and deploying RESTful web services and microservices architectures for applications involving ML.
• Proficiency with UNIX environments, Git for version control, and Jenkins for CI/CD processes.

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