Staff Machine Learning Engineer, Growth
New York, New YorkFull-TimeStaffAI / Data Science
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.
