Senior Machine Learning Platform Engineer

Description

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 Joining Hinge’s AI Platform Core team offers a unique opportunity to help define and contribute to a growing ML platform with the goal of accelerating the development, deployment, and operations of AI enabled features on Hinge. If you are excited by the idea of working on challenges ranging from maturing and scaling our training and serving frameworks to enable millisecond real-time predictions and models that continually learn at scale, providing automatic model performance monitoring & debug frameworks, and building Platforms for architecting and evaluating generative AI & Agentic systems, then read on! With a mission to democratize AI for Hinge, ensuring it remains easily accessible, robust, scalable, cost-efficient, and trusted. You'll collaborate across our product and platform teams, as well as with external stakeholders like data science and backend engineering. You will meet our internal customers where they are and optimize towards their needs by delivering value incrementally and coupled to their problems. Being part of a small yet impactful team means having a broad scope of responsibility. As the role of ML at Hinge is only becoming more impactful, this role provides you with a chance to grow in impact and scope across the company. This is an exciting opportunity to own and help define the future of machine learning within a growing and maturing team! Responsibilities Own or contribute to technical designs for the feature platform, training platform, serving platform, AI observability platform and GenAI Platform and underlying operational infrastructure to enable Product impact. Develop, maintain, and enhance frameworks for AI/ML model development and deployment while establishing and driving best practices in MLOps / GenAI engineering. Design, advocate, and implement for usability, reliability, scalability, operational excellence, and cost management while delivering incrementally. Collaborate closely with ML Engineers, Data Scientists, Data Engineers and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process. Mentor and educate ML Engineers, Backend Engineers and Product Managers on current and up and coming tools and technologies for ML operations & GenAI product development through presentations and documentation. Help design and architect an AI platform that adheres to the principles of responsible AI (Authenticity, Transparency, Equity) and builds privacy compliance into the platform. Lead build vs buy discussions on technologies that underpin the Platforms we serve. Participate in on-call and Incident Management processes. What We're Looking For 4+ years of experience, depending on education, as an ML, backend, data, or platform engineer developing and working with large scale, complex systems. 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes 1+ year of experience leading projects with at least 1 other team member through completion. 1+ year of experience for Senior designing and developing online and production grade ML systems. A degree in computer science, engineering, or a related field. Strong programming skills: Proficiency in languages like Python, Go, or Java. System design & architecture : Ability to design scalable and efficient ML systems. Cloud platform proficiency : The ability to utilize cloud environments such as GCP, AWS, or Azure. ML knowledge : A basic understanding of ML algorithms, techniques, and best practices. Data engineering knowledge : Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage 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.