Job Description

Position Overview

• Location Type: Remote
• Employment Type: Full-time
• Salary: Not specified

Hinge is the dating app designed to be deleted. In today’s digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With tens of millions of users across the globe, we’ve become the most trusted way to find a relationship, for all.

About the Role: The Dating Outcomes group is responsible for making sure people get to see their most compatible matches as well as helping them better present themselves or even start a conversation when they match with someone. In short, we help people go on dates! We are hiring ML practitioners to help us build the foundations of an AI-first dating experience using the latest advancements in the field, leveraging Hinge’s years worth of preference data. You can expect to work on recommendation systems end-to-end, experiment with using LLMs, photo and mixed input embedding models, as well as building and deploying real-time predictive models that directly impact millions of users' experience. This is a fast-growing team, and you will get a chance to own and define the strategy, vision, and plan for how to accelerate machine learning at Hinge.

Requirements

• Strong programming skills: Proficiency in languages like Python, Java, or C++.
• System design & architecture: Proven track record of 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 ML serving solutions like Ray, KubeFlow, or W&B is a plus.
• ML knowledge: Deep understanding of DNN architectures, track record of building, debugging, and fine-tuning models. Familiarity with PyTorch, TF, knowledge distillation, and recommender systems are a plus.
• Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure such as Kubernetes and Terraform.
• Data engineering knowledge: Understanding of data pipelines and processing.

Responsibilities

• Own and contribute to foundational models (e.g., CLIP embeddings) that power our recommendations pipelines.
• Contribute to the research and development of recommender models and experiment with the latest ML innovations (e.g., LLM agents and transcription models).
• Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally.
• 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.
• Mentor and educate ML Engineers on current and up-and-coming research, technologies, and best practices for doing ML at scale.
• Perform other job-related duties as assigned.

Application Instructions

• Not specified