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Participate in our on-call rotation to ensure system reliability, promptly addressing issues to ensure smooth operations. 5+ years of professional/industry experience in data engineering, delivering data products from conception to delivery and supporting their underlying infrastructure. Strong technical background in building and optimizing data pipelines, with proficiency in Python, SQL, and data modeling for different types of data sources and business processes. Technical expertise in the following: Python, Go, Kubernetes, Docker, Terraform, CircleCI, GitHub Actions, Kafka, PubSub, Dataflow, Redshift, BigQuery, Databricks, dbt, Looker Experience with cloud environments such as GCP, AWS, or Azure Strong communication skills (written and verbal), with an ability to work collaboratively with cross-functional teams, including backend engineering, data science, and product teams. 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Improve user engagement with notifications by optimizing for relevancy and personalizing for responsiveness. Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters. Keep abreast of and bring to Hinge applicable cutting-edge research, technologies, and best practices in the ML/AI space. Ensure the ethical and responsible use of ML/AI and compliance with privacy regulations to protect user data. Communicate effectively to audiences of various technical and non-technical backgrounds. Proficiency in languages like Python, Java, or C++ Domain expertise: Deep understanding of machine learning model architectures, DNN experience a big plus. Proven track record of building, debugging, and fine-tuning machine learning models for user facing products. System design & architecture: Experience training and deploying large scale ML models especially DNNs. Good understanding of distributed computing for learning and inference. Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with Ray, KubeFlow, or Weights & Biases is a plus. Data engineering knowledge: Skills in handling and managing large datasets including data cleaning, preprocessing, and storage. Deep understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow. Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.. Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes. 4+ years of experience as an MLE, previous experience in User Growth or Monetization is preferred. 2+ years of experience working on a cloud environment such as GCP, AWS, or Azure. 2+ years of experience designing and developing online and production grade ML systems. 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