Staff Machine Learning Engineer, Dating Outcomes

New YorkFull-timelead

Data ScienceOtherStaff Machine Learning EngineerMachine LearningData & AI

0 views0 saves0 applied

Apply Now

Quick Summary

Overview

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

Technical Tools

Data ScienceOtherStaff Machine Learning EngineerMachine 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 Staff 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.

Design and own foundational research and models that power our recommendations ecosystem.

Identify the next step change in our technical capabilities, conduct state-of-the-art applied research, and move them into production using best practices in machine learning engineering.

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

Collaborate closely with Directors and VP+ to understand and shape Hinge’s strategic direction, as well as work with other Machine Learning Engineers, Product Managers, Data Engineers, and Scientists to make that a reality.

Coach, 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++ and a deep understanding of low-level deep learning computation fundamentals.

System design & architecture: Proven track record of research, training, and deployment of DNNs at scale. Deep 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 various DNN architectures, track record of building, debugging, and fine-tuning models.

DevOps skills: Track record of deploying, managing, and orchestrating offline and online deep learning models at scale.

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. Proven ability to influence company strategy and direction is a plus.

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.

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

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

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

3+ years of experience leading projects with at least 2 other team members through completion.

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