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About the Role
Join Hinge as a Senior Machine Learning Engineer, where you'll lead the application of AI and machine learning to effectively mitigate the impact of bad actors, remove policy violating content, and ensure user safety on the platform. Working closely with an expanding Trust & Safety team, you will collaborate with product managers, data scientists, engineers, and analysts to develop impactful AI/ML solutions. In this high-impact position within a small, dynamic team, you'll have the opportunity to play a foundational role in shaping how Hinge utilizes AI and machine learning in various contexts. Your expertise will be instrumental in creating a safer and more meaningful user experience on their journey to find an intimate connection.
Responsibilities
- Develop, deploy, and maintain end-to-end machine learning models to identify and mitigate bad actors, remove policy-violating content, and ensure user safety.
- Design and implement scalable systems (e.g., using Spark, Kubernetes) to preprocess data, run inference, and manage post-processing pipelines.
- Define standardized performance metrics, testing protocols, and evaluation processes to measure the effectiveness, identify and mitigate potential risks, and ensure fairness of AI solutions.
- Ensure ongoing assessment and refinement of AI solutions, incorporating user feedback, business impact, and emerging ethical considerations.
- Collaborate closely with Data Scientists, Data Engineers, Product Managers, Backend Engineers, and the AI Platform Team to ensure a comprehensive and coordinated approach to user safety.
- Stay abreast of new trends and research in AI/ML that can be applied to Trust & Safety initiatives to improve detection, user safety, and stay ahead of potential threats.
What We're Looking For
- Strong programming skills: Proficiency in languages like Python, Java or C++ and SQL, proficiency in at least one ML stack (e.g., PyTorch), and strong understanding of data pipelines
- Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning real-time machine learning models for user facing products. Experience with applying expertise to fraud detection, content moderation, or related fields is a plus .
- System design & architecture: Experience training and deploying large scale ML models. Good understanding of distributed computing for learning and inference.
- Evaluation frameworks for LLMs: Experience designing robust testing protocols to ensure effectiveness, fairness, and safety of AI-driven features.
- Cloud and data platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with solutions like Databricks, Ray, or KubeFlow is a plus.
- Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Good understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow.
- Strategic technical leadership skills: Demonstrated track record of guiding teams through complex ML projects in alignment with product and business objectives.
- 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.
Prior Experience
- 4+ years of experience, depending on education, as an MLE or data scientist. Previous experience working in Trust & Safety or related fields (e.g., fraud detection, content moderation, compliance) is preferred.
- 2+ years of experience in applying end-to-end machine learning models, including data collection, model training, deployment, and monitoring in an industry setting
- 1+ years of experience integrating LLMs in real-world applications with the appropriate baseline metrics and evaluation methodologies.
- 1+ years of experience utilizing ML infrastructure components such as a feature store, model training environment, model serving environment, observability, workflow orchestrator, etc.
- A degree in computer science, engineering, or a related field (or equivalent practical experience).
