Staff Machine Learning Engineer, Notifications Relevance in United States at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Machine Learning Engineer, Notifications Relevance based in the United States.

This role sits at the core of large-scale personalization and recommendation systems, focused on delivering highly relevant notifications that connect users with the right content at the right moment. You will help shape how millions of users discover conversations, communities, and updates through push, email, and in-app channels. As a senior technical leader, you will define strategy and architecture for notification relevance systems spanning retrieval, ranking, optimization, and measurement. The role combines deep machine learning expertise with product impact, requiring you to operate at scale across high-traffic systems. You will work closely with product, infrastructure, and data science teams to build and evolve next-generation recommendation pipelines. This is a high-impact opportunity to influence engagement and retention through advanced ML and emerging generative AI capabilities.

• Design and develop large-scale recommendation and personalization systems that improve notification relevance and user engagement.
• Define and lead the technical vision and roadmap for notification targeting, ranking, and delivery optimization.
• Build and enhance machine learning models for retrieval, ranking, and budget optimization across notification channels.
• Deploy and operate ML systems in production, ensuring scalability, reliability, and strong monitoring practices.
• Integrate LLMs and generative AI techniques into recommendation pipelines to improve personalization quality.
• Serve as a domain expert in ML architecture, driving key decisions across distributed systems and infrastructure.
• Collaborate with product, engineering, data science, and infrastructure teams to solve complex cross-functional challenges.
• Improve measurement frameworks to evaluate model performance, engagement, and user experience impact.
• Identify opportunities for system improvements and lead experimentation for new ML approaches.
• Ensure end-to-end ownership of models from design and training through deployment and iteration.
• Contribute to platform evolution, improving scalability, efficiency, and robustness of recommendation systems.

• 8+ years of industry experience in machine learning, with strong focus on large-scale recommendation systems.
• Proven experience designing and deploying ML models in production environments at scale.
• Strong expertise in deep learning frameworks such as PyTorch or TensorFlow.
• Experience working with LLMs and generative AI in production systems.
• Proficiency in programming languages such as Python and/or Golang.
• Strong understanding of ranking systems, retrieval systems, and personalization algorithms.
• Experience defining technical roadmaps and driving ML system improvements across teams.
• Familiarity with distributed systems, experimentation frameworks, and model evaluation techniques.
• Strong product intuition and ability to translate business goals into ML solutions.
• Excellent collaboration and communication skills across technical and non-technical stakeholders.
• Big plus: experience with cutting-edge model architectures and agentic AI frameworks.

• Competitive base salary aligned with senior ML engineering market standards.
• Equity compensation in the form of restricted stock units.
• Comprehensive healthcare coverage including medical, dental, and vision insurance.
• 401(k) retirement plan with employer match.
• Flexible vacation policy and paid volunteer time off.
• Generous parental leave and family planning support.
• Mental health, coaching, and wellbeing programs.
• Gender-affirming care and inclusive healthcare benefits.
• Remote-first work environment with global flexibility.
• Professional development and learning support programs.
• Modern tooling environment supporting AI-augmented engineering workflows.