Machine Learning Engineer, Dating Outcomes

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 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 ensuring that people see their most compatible matches, helping them present themselves more effectively, and start meaningful conversations when they match. In short, we help people go on great dates! We are hiring Machine Learning Engineers to help us build the foundations of an AI first dating experience using the latest advancements in the field, leveraging Hinge’s years of preference data. You can expect to work on modeling work end to end, including work on recommendation systems, multi-modal embedding, and generative models. You will be moving your research into production, both offline and real-time inference endpoints that directly impact the experience of millions of users. This is a fast-growing team, and you will have the opportunity to work with and learn from senior researchers and engineers. Responsibilities

Contribute to the research and development of models powering Hinge and experiment with the latest innovations in the field of Machine Learning (e.g., LLM agents, MMoE models, VAEs, etc.)

Build systems with availability, scalability, operational excellence, and cost management in mind.

Collaborate closely with other Machine Learning Engineers, Product Managers,  Data Engineers, and Scientists to understand our users' needs and identify opportunities to make their experience better through machine learning.

Work within our AI platform team to move models to production, monitor them, and make improvements to our serving infrastructure.

What We're Looking For

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 specially 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, recommender systems are a plus.

Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure such as Kubenetes and Terraform.

Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Deep 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..

Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation

Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.

2+ years of experience, depending on education, as an MLE.

1+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes

1+ years of experience designing and developing online and production grade ML systems.

A degree in computer science, engineering, or a related field.