Original listing text, shown exactly as published by the company.
About the Role
We are hiring a Staff Machine Learning Engineer to drive the design, development, and deployment of machine learning systems that power our core products in User Growth and Monetization. You will work on building deeply personalized user experiences that drive business growth and meet individual needs and preferences: by targeting the right users with the right products at the right time, we empower product teams to drive meaningful growth in revenue and user engagement while enhancing user satisfaction.
The Growth Product Group is responsible for the development of paid features to accelerate dating outcomes, driving engagement throughout a user’s lifecycle and making Hinge the dating destination for all communities. You can expect to work on a broad range of problems, from identifying how to send the right message to the right user at the right time to optimizing the efficacy of our paid offerings. This is a fast growing team, and you will help define the vision and strategy that drives meaningful growth and accelerates machine learning at Hinge.
Responsibilities
- Lead the end-to-end development of production-grade ML systems such as user targeting models that will help drive engagement, improve dating outcomes and/or improve user adoption of and engagement with paid features
- Define and own the technical roadmap for ML within your product area and align with company-wide priorities
- Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process
- Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally
- Keep abreast of and bring to Hinge applicable cutting-edge research, technologies, and best practices in the ML/AI space.
- Mentor and educate ML Engineers on current and up and coming research, technologies and best practices of doing ML at scale.
- 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
- Strong programming skills: Proficiency in Python and ML libraries such as PyTorch
- Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning machine learning for user facing products. Experience with causal inference, uplift modeling, and interventional data collection is a plus.
- System design & architecture: Strong background in setting up and optimizing ML infrastructure, including containerization (Docker), orchestration (Kubernetes), and CI/CD workflows for ML (e.g., model versioning, automated testing).
- Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow, or Weights & Biases is a plus.
- Data engineering knowledge: Skills in handling and managing large datasets including data cleaning, preprocessing, and storage. Good 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..
- Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.
- 5+ years of experience, depending on education, as an MLE, with at least 2 years in a senior or staff-level role
- Previous experience in User Growth or Monetization
- 3+ years of experience designing and developing end-to-end, production grade ML systems
- 4+ years of experience working in a cloud environment such as GCP, AWS, Azure
- 3+ years of experience leading projects with at least 1 other team member through completion.
- A degree in computer science, engineering, or a related field or equivalent experience.
