Responsibilities:
- Develop and deploy targeting and timing models that underlie a smart notifications system that guides and influences user behavior to improve dating outcomes.
- 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.
What We're Looking For:
- 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.
- A degree in computer science, engineering, or a related field, or equivalent experience.
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