Machine Learning Engineer

Overview

Hinge is the dating app designed to be deleted. We’re on a mission to inspire intimate connection to create a less lonely world. Our success is defined by setting up great dates, with tens of millions of users worldwide.

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 build 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. This enables product teams to drive growth in revenue and user engagement while enhancing user satisfaction.

The Growth Product Group develops paid features to accelerate dating outcomes, driving engagement across a user’s lifecycle and making Hinge the dating destination for all communities. You will work on a broad range of problems, from identifying the right messages to the right users at the right time to optimizing the efficacy of paid offerings. This is a fast-growing team where you will help define the vision and strategy that drives meaningful growth and accelerates machine learning at Hinge.

• Lead the end-to-end development of production-grade ML systems such as user-targeting models that drive engagement, improve dating outcomes, and/or improve adoption and engagement with paid features
• Define and own the technical roadmap for ML within your product area and align with company-wide priorities
• Collaborate with ML Engineers, Data Scientists, and Product Managers to understand needs and identify opportunities to accelerate AI/ML development and deployment
• Design and implement for availability, scalability, operational excellence, and cost management while delivering incremental impact
• Stay current with cutting-edge ML/AI research, technologies, and best practices and bring them to Hinge
• Mentor ML Engineers on current and emerging research, technologies, and best practices for ML at scale
• Ensure ethical and responsible use of ML/AI and compliance with privacy regulations to protect user data
• Communicate effectively to audiences of varied technical backgrounds

• Strong programming skills: Proficiency in Python and ML libraries such as Py Torch
• Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning ML for user-facing products. Experience with causal inference, uplift modeling, and interventional data collection is a plus.
• System design & architecture: Strong background in ML infrastructure, including Docker, Kubernetes, and CI/CD workflows for ML (model versioning, automated testing)
• Cloud platform proficiency: Experience with GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, Kubeflow, or Weights & Biases is a plus.
• Data engineering knowledge: Experience handling large datasets, data cleaning, preprocessing, and storage. Understanding of batch and streaming pipelines and orchestrators like Argo and Airflow.
• Collaboration and communication: Ability to work effectively in a team and communicate complex ideas clearly to technical and non-technical audiences
• Software leadership: Track record of leading projects to completion with measurable outcomes
• 5+ years as an MLE, with at least 2 years in a senior or staff-level role
• Experience in User Growth or Monetization
• 3+ years designing and developing end-to-end, production-grade ML systems
• 4+ years experience in a cloud environment (GCP, AWS, Azure)
• 3+ years leading projects with at least 1 other team member
• A degree in computer science, engineering, or related field, or equivalent experience

$ - $ per year. This range is reflective of a position based in New York City and may be adjusted for geographic location. Factors include scope and responsibilities, candidate experience, education, market considerations, and internal equity.

The Hinge values include Empathy. When personal values align with company values, it’s the right place to work. We offer compensation packages with base salary, annual bonus target, and MTCH equity. Other benefits include:

The Hinge culture emphasizes transparency, candor, humility, and radical inclusion. We have ERGs and a focus on delivering outstanding results.

We value in-person collaboration for full-time and hybrid roles. The NYC office offers space to work together, play bocce on the terrace, and a collaborative environment.

If you’re passionate about inspiring intimate connections, join us.

Hybrid roles in NYC require weekly in-person presence in the Meatpacking District. We aim for consistent in-office days to maximize collaboration. The office features a vibrant space with plants, good food, and snacks.