Senior Machine Learning Engineer, Dating Outcomes

New YorkFull-timesenior

Data ScienceMachine Learning EngineerDataMachine LearningData & AI

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Overview

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge,

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Data ScienceMachine Learning EngineerDataMachine LearningData & AI

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 Senior 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 build recommendation systems end to end, experiment with using LLMs, photo and mixed input embedding models, as well as build and deploy real-time predictive models that directly impact the experience of millions of users. This is a fast-growing team, and you will have the opportunity to own and define the strategy, vision, and plan for accelerating machine learning at Hinge.

Own and contribute to foundational models that power our recommendations ecosystem.

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.)

Design, advocate for, and implement solutions that ensure availability, scalability, operational excellence, and cost management, while delivering incremental impact to our daters.

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.

Mentor and educate Machine Learning Engineers on current and SOA research, technologies, and best practices of practicing machine learning at scale.

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, data processing, and inference.

Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, Databricks, 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 is a plus.

DevOps skills: The ability to establish, manage, and use data and compute infrastructure such as Kubernetes 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.

Prior Experience

4+ years of experience, depending on education, as a Machine Learning Engineer.

2+ years of experience working in a cloud environment, such as GCP, AWS, or Azure, and with DevOps tooling, including Kubernetes.

2+ years of experience designing and developing online and production grade machine learning systems.

1+ year of experience leading projects with at least 1 other team member through completion.

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