Research Engineer, Notifcations
Description
The ChatGPT team works across research, engineering, product, and design to bring OpenAI’s technology to the world.
We seek to learn from deployment and broadly distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. We aim to make our innovative tools globally accessible, transcending geographic, economic, or platform barriers. Our commitment is to facilitate the use of AI to enhance lives, fostered by rigorous insights into how people use our products.
About the Role
We are looking for a Machine Learning Engineer to join our Notifications team, focused on building and scaling intelligent notification systems that provide real value to users even when they are not actively on the product. This role will be central to shaping how ChatGPT communicates proactively and helpfully with users—surfacing the right content, at the right time, through the right channel.
You will work on designing and implementing ranking and recommendation systems that leverage both classical ML techniques and large language models (LLMs) to optimize notification relevance, timeliness, and user experience. The ideal candidate has strong ML fundamentals, experience shipping ranking or recommendation systems in production, exposure to LLMs, and sharp product intuition. You should be comfortable operating across research and product boundaries: thinking from first principles, running rigorous experiments, and building scalable systems.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
In this role, you will:
Design and build end-to-end ranking and recommendation systems for notifications, from modeling to evaluation and deployment.
Apply and adapt LLMs to ranking problems, including prompt-based approaches and fine-tuning.
Develop experiments to evaluate notification relevance, user value, and long-term impact, working closely with product, data science, and engineering teams.
Collaborate with research teams to leverage the latest modeling techniques while balancing practical constraints of production systems.
Build robust offline and online evaluations to measure system improvements and user outcomes.
Contribute to the broader Growth ML stack and help set technical direction for intelligent user engagement systems.
Qualifications
Have hands-on experience building and deploying ranking, recommendation, or personalization systems at scale.
Have a deep understanding of machine learning and its applications, with exposure to LLMs and their integration into product experiences.
Are experienced in experimentation, A/B testing, and analyzing impact on user behavior and business metrics.
Are comfortable diving into large ML codebases, designing evaluations, and debugging complex modeling issues.
Thrive in dynamic, fast-changing environments and can think from first principles to design elegant solutions.
Have strong product intuition and can balance modeling sophistication with product impact.