Computer Scientist 2
Adobe · Bengaluru · 8–12 yrs experience · Posted 2026-06-23
Tech stack: Express, Kafka
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About the role
The Opportunity Adobe Express empowers people and teams to build standout content with ease, and the AI Foundations team is what makes that possible at scale. We develop the core AI platform that fuels creativity across design, imaging, motion, and personalization, serving millions of users worldwide. We are hiring a Computer Scientist 2 to contribute to the development and scaling of AI infrastructure powering these experiences. This position fits well for candidates with strong software engineering foundations, experience with ML systems, and a sincere interest in building stable, high-performance platforms for current AI products. You'll work alongside experienced engineers to build production systems that bring Agentic AI, Compose AI, Imaging AI, Motion AI, and Personalization AI to life spanning model orchestration, inference services, data pipelines, storage and caching layers, session analytics, and continuous evaluation frameworks. What You'll Do Build and improve core platform components that power intelligent experiences across Adobe Express. Contribute to data and inference pipelines supporting training, evaluation, fine-tuning, and deployment of ML models. Develop backend services, microservices, and workflows that connect models, APIs, data systems, and product surfaces. Help maintain and improve runtime systems for inference and orchestration, with a focus on reliability, observability, and performance. Work on storage, caching, and data-access patterns to improve efficiency, scalability, and cost-effectiveness. Collaborate closely with engineers, researchers, and product teams to ship production-ready AI capabilities. Participate in debugging, testing, monitoring, and operational improvements across AI platform services. What You'll Need to Succeed 8 - 12 years of experience in software engineering, backend infrastructure, data systems, or ML infrastructure. Solid understanding of distributed systems, backend services, and scalable system development. Hands-on experience building or supporting APIs, data pipelines, or event-driven systems. Comfort working in cloud environments with production engineering and service deployment practices. Experience with ML systems or applications based on LLMs, model inference, orchestration, or evaluation is highly advantageous. Strong problem-solving instincts and a collaborative approach. Clear communication skills and an eagerness to learn from cross-functional partners. Preferred Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related technical field, or equivalent experience. Exposure to generative AI systems - LLMs, multimodal models, or diffusion models. Familiarity with MLOps practices: experiment tracking, model deployment, or evaluation workflows. Curiosity about agentic AI concepts such as tool use, task planning, or memory systems. Experience with technologies such as Kafka, Spark, Flink, or similar distributed data frameworks.
Qualifications
- 12 years of experience in software engineering, backend infrastructure, data systems, or ML infrastructure.
- Solid understanding of distributed systems, backend services, and scalable system development.
- Hands-on experience building or supporting APIs, data pipelines, or event-driven systems.
- Comfort working in cloud environments with production engineering and service deployment practices.
- Experience with ML systems or applications based on LLMs, model inference, orchestration, or evaluation is highly advantageous.
- Strong problem-solving instincts and a collaborative approach.
- Clear communication skills and an eagerness to learn from cross-functional partners.
- Preferred Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related technical field, or equivalent experience.
- Exposure to generative AI systems
- LLMs, multimodal models, or diffusion models.
- Familiarity with MLOps practices: experiment tracking, model deployment, or evaluation workflows.
- Curiosity about agentic AI concepts such as tool use, task planning, or memory systems.
- Experience with technologies such as Kafka, Spark, Flink, or similar distributed data frameworks.
Responsibilities
- Build and improve core platform components that power intelligent experiences across Adobe Express.
- Contribute to data and inference pipelines supporting training, evaluation, fine-tuning, and deployment of ML models.
- Develop backend services, microservices, and workflows that connect models, APIs, data systems, and product surfaces.
- Help maintain and improve runtime systems for inference and orchestration, with a focus on reliability, observability, and performance.
- Work on storage, caching, and data-access patterns to improve efficiency, scalability, and cost-effectiveness.
- Collaborate closely with engineers, researchers, and product teams to ship production-ready AI capabilities.
- Participate in debugging, testing, monitoring, and operational improvements across AI platform services.