Staff Machine Learning Engineer, Notifications Relevance
New
Based in the United StatesFull-TimeStaff
SalaryCompetitive base salary aligned with senior ML engineering market standards.
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonMachine LearningPyTorchGoTensorflowGenerative AIDistributed Systems
Requirements
- 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.
Responsibilities
- 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.
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