Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development using one or more general-purpose programming languages (e.g., Python, Java, C , Go).
- 3 years of experience testing and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical field.
- Experience with ML frameworks (e.g., TensorFlow, JAX) and production ML platforms (e.g., TFX, AdBrain).
- Experience with large-scale data processing and distributed systems, and with data structures and algorithms.
- Experience with generative models and their applications.
- Experience working with and mentoring junior engineers.
- Familiarity with online advertising systems, creative optimization, personalization, or recommender systems.
About the jobAs a Senior Software Engineer in Creative Composition Intelligence, you will be driving the design, development, and productionization of advanced Machine Learning models for optimizing Ad creative composition and delivery. You will lead efforts to accelerate innovation in how we automatically construct and select the best creative elements for any given context.
In this role, you will take on new, ambiguous problems at the intersection of GenAI, ML, large-scale distributed systems, and Ad product needs. You will be expected to set technical direction, influence across teams, and mentor other engineers, as well as in creating and delivering impactful systems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Lead end-to-end development of novel machine learning models, incorporating techniques like deep learning, reinforcement learning, and generative AI, from concept to production.
- Define the technical strategy and roadmap for key ML components, ensuring alignment with product goals and driving measurable impact through experimentation.
- Build and integrate scalable ML pipelines and services, collaborating with infrastructure and serving teams to power creative optimization.
- Analyze performance metrics, deriving insights, and implementing algorithmic improvements for creative selection, freshness, and exploration.
- Mentor engineers and promote best practices in applied machine learning and system design.
