Staff Machine Learning Engineer, Growth

New York, New YorkFull-TimeStaffAI / Data Science

Skills

Python AWS GCP Azure Kubernetes Docker CI/CD Machine learning Deep learning PyTorch Mentoring Ownership Airflow Kubeflow

You will be redirected to the company career page

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.