Machine Learning Engineer, Trust & Safety

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 Join Hinge as a Machine Learning Engineer on the Trust & Safety team, where you'll apply AI and machine learning to mitigate the impact of policy violating actors, remove harmful content, and keep our users safe on the platform. Working alongside an established Trust & Safety AI team, you'll partner with product managers, data scientists, engineers, and analysts to build, deploy, and improve detection systems that have a direct, measurable impact on user safety. This is a high-ownership IC role on a small team with a large mission. You'll own the end-to-end lifecycle of ML projects, contribute to a growing portfolio of AI-powered safety features, and grow alongside senior engineers, all while playing a foundational role in how Hinge applies machine learning to give users a safer experience on their journey to find intimate connection. Responsibilities Build, deploy, and maintain end-to-end machine learning models that detect policy violating actors, remove harmful content, and keep users safe. Own medium-sized projects end-to-end (from problem scoping and data collection to training, deployment, monitoring, and iteration). Contribute to scalable inference and data pipelines (e.g., Spark, Kubernetes), including preprocessing, batch and real-time inference, and post-processing components. Apply standardized performance metrics, testing protocols, and evaluation processes to measure model effectiveness and identify risks. Continuously assess and refine deployed models using user feedback, business impact signals, and emerging policy and ethical considerations. Collaborate closely with Data Scientists, Data Engineers, Product Managers, Backend Engineers, and the AI Platform team to ship coordinated user safety improvements. Stay current on advances in AI/ML, particularly LLMs and evaluation methods, and apply appropriate techniques to detection and user safety problems. What We're Looking For Strong programming skills: Proficiency in Python and SQL, comfort with at least one ML stack (e.g., PyTorch, Hugging Face Transformers), and a working understanding of data pipelines. Domain expertise: Solid understanding of machine learning, deep learning, and emerging AI techniques. Track record of building, debugging, and fine-tuning ML models for user-facing products. Experience with content classification, fraud detection, or related Trust & Safety problems is a plus. System design & architecture: Experience training and deploying ML models in production. Working understanding of distributed computing for inference and training. AI application: Hands‑on with AI agents, RAG, structured outputs, and function/tool calling. Understands when to reach for an agent or LLM vs. a classical ML approach. Evaluation frameworks for ML and LLM systems: Comfort designing and running evaluations to measure model effectiveness, fairness, and safety across both classical ML and LLM systems. Familiar with golden datasets, LLM‑as‑judge patterns, precision/recall/F1, false positive rates, hallucination, and offline/online metrics. Cloud and data platform proficiency: Hands‑on with at least one cloud environment (GCP, AWS, or Azure). Familiarity with Databricks, Ray, or Kubeflow is a plus. Data engineering knowledge: Comfortable handling large datasets, including cleaning, preprocessing, and storage, and contributing to batch and streaming pipelines orchestrated with tools like Databricks or Argo. Project ownership: Demonstrated ability to drive medium‑sized projects to completion, identify and unblock yourself on technical issues, and ship measurable outcomes with limited oversight. 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 Prior Experience 2+ years of experience as an MLE, applied scientist, or data scientist (depending on education). 1+ years of experience applying end-to-end machine learning models in an industry setting—including data collection, model training, deployment, and monitoring. Experience integrating or evaluating LLMs in real‑world applications with appropriate baseline metrics and evaluation methodologies is a plus. Familiarity with at least one ML infrastructure component (feature store, training environment, model serving, observability, workflow orchestrator). Previous exposure to Trust & Safety, fraud detection, content classification, or compliance is preferred but not required. Demonstrated use of modern AI tooling in their actual workflow (e.g., Cursor, Claude Code, Codex). A degree in computer science, engineering, or a related field (or equivalent practical experience). $150,000 - $198,000 a year Factors such as scope and responsibilities of the position, candidate's work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary range is reflective of a position based in New York City. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting. Benefits 401(k) Matching: We match 100% of the first 10% of pre‑tax 401(k) contributions you make, up to a maximum of $10,000 per year. Professional Growth: Get an annual Learning & Development stipend once you’ve been with us for three months. You also get free access to Udemy, an online learning and teaching marketplace with over 6000 courses, starting your first day. Parental Leave & Planning: When you become a new parent, you’re eligible for 100% paid parental leave (20 paid weeks for both birth and non‑birth parents.) Fertility Support: You’ll get easy access to fertility care through Carrot, from basic treatments to fertility preservation. We also provide a stipend towards fertility preservation. You and your spouse/domestic partner are both eligible. Date Stipend: All Hinge employees receive a $100 monthly stipend for epic dates– Romantic or otherwise. Hinge Premium is also free for employees and their loved ones. ERGs: We have eight Employee Resource Groups (ERGs)—Asian, Unapologetic, Disability, LGBTQIA+, Races, Women/Nonbinary, Parents —that hold regular meetings, host events, and provide dedicated support to the organization & its community. At Hinge, our core values are… Authenticity: We share, never hide, our words, actions and intentions. Courage: We embrace lofty goals and tough challenges. Empathy: We deeply consider the perspective of others. Diversity inspires innovation Hinge is an equal‑opportunity employer. We value diversity at our company and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We believe success is created by a diverse workforce of individuals with different ideas, strengths, interests, and cultural backgrounds. If you require reasonable accommodation to complete a job application, pre‑employment testing, or a job interview or to otherwise participate in the hiring process, please let your Talent Acquisition partner know. #J-18808-Ljbffr