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Oversee legal counsel for Hinge's marketing and communications campaigns, promotions and partnerships from conception through execution, including providing advice on advertising laws, regulatory compliance, rights clearances and marketing claims. Serve as a legal lead to Hinge's trust & safety team providing legal counsel and transactional support for customer care and education, tooling and vendor, and QA programs and initiatives. Provide legal counsel to Hinge's user research team, including advising on the design and implementation of research programs. Support Hinge's compliance and regulatory response work, in liaison with Match Group legal, privacy and trust & safety central teams, in connection with state and global regulations such as DSA, UK OSA, EU AI Act and the EU Accessibility Act. Partner cross-functionally with Match Group's privacy, government relations, trust and safety, and communications teams to ensure our internal and external efforts are aligned with Hinge and Match Group's values. Collaborate with internal Match Group (i) intellectual property counsel to support establishing and protecting Hinge's global trademark and IP portfolio, and (ii) litigation counsel to support litigation and regulatory matters. Draft and negotiate commercial agreements, including marketing, SAAS, technology, data processing, agency, content, production, social media, consumer insights, analytics, communications, consulting and other general vendor and operations agreements. Advise and manage workflow process improvement, playbook development, business team training, and template use to address the evolving needs of a growing organization. Assist in the administration of corporate policies and procedures, such as vendor security, privacy compliance, and contract approval. Manage the evolution and implementation of Terms of Use, content policies, disclosures, FAQs, and other user-facing legal policies and information. 7+ years' experience, with both top law firm and in-house legal department experience preferred. Law degree from a top accredited law school and admission (or in-house eligibility) to practice law in New York. Direct experience counseling technology company stakeholders on intellectual property, information security, privacy, biometric and AI regulations in B2C digital contexts such as social discovery platforms, marketplace businesses, user-generated content, and/or regulated products and/or consumer facing technology. Excellent business communication and analytical skills, as well as an impeccable attention to detail and strong sense of ownership and accountability. Comfortable with and interested in using AI tools (e.g., ChatGPT, Gemini, Zoom AI Companion) to enhance drafting, research, and workflow efficiency. Curious and practical-minded legal partner skilled in helping business teams to identify and balance risks against business priorities. Professional ease in working collaboratively with Hinge employees as well as cross-functionally with Match Group's Legal and other shared services functions. 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Budget Ownership: Oversee the Insights & Strategy budget, track spend, and guide investment decisions across research, partnerships, and tools. Resource & Capacity Planning: Partner with Insights & Strategy leadership to plan team bandwidth and resourcing, keeping the group focused on the highest-impact work. Agency and Vendor Management: Lead day-to-day relationships with external research and strategy partners, from scoping and contracting to ways of working. Partner with Legal and Procurement on contracts and compliance. In-House Research Operations: Scope and manage custom, in-house research projects from start to finish, ensuring rigor in methodology, compliance, recruitment, and cross-functional alignment. Strategic Planning & Roadmap Design: Partner with Insights & Strategy leadership on annual and quarterly planning. Determine what work should be done in-house vs. with external partners, sequence priorities thoughtfully, and keep the team focused on what matters most. Insight Activation: Create engaging ways to share insights across Hinge \u2014 from leadership sessions to company-wide moments \u2014 turning understanding into action. Systems & Continuous Improvement: Refine the tools, processes, and documentation that keep work transparent and effective. Make it easier for the team to see progress, learn from outcomes, and celebrate impact. Operational : 8+ years in operations, project management, or strategy roles \u2014 ideally within marketing, research, or insights \u2014 with a proven experience managing complex, multi-stakeholder initiatives from brief to delivery. Collaborative Strategist: A systems thinker and problem-solver who partners closely with researchers, strategists, and marketers to turn insight into action and bring structure to creativity. Mission-Driven: Passionate about Hinge's mission to inspire intimate connection and create a less lonely world. Experience with research and strategy teams or agencies. Familiarity with tools like Asana or Monday.com. 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Partner with design, research, data science, and engineering to explore how personalization and empathetic design can improve outcomes. Use data and user insights to guide decisions and shape a focused roadmap. Work closely with engineering to translate ideas into high-impact product solutions. Communicate priorities and tradeoffs clearly across teams and leadership. Collaborate across Hinge to ensure a cohesive, emotionally intelligent, trustworthy product experience. Partner with Marketing to align product and go-to-market strategies that attract and activate new users, including those new to dating apps. Coach and mentor other PMs on structured discovery, decision-making, and hypothesis-led development. 8+ years of product management experience in consumer tech, ideally in personalization or social-driven products. A track record of 0-1 discovery and launches that drive measurable outcomes. Experience building adaptive or ML-informed experiences that solve cold-start challenges. Expertise at running hypothesis-driven experiments and making decisions with limited data. Bias for action and ability to lead multi-quarter workstreams with clarity and momentum. Strong intuition for user experience, storytelling, and emotional design. Excellent communicator who brings clarity to complex spaces and influences across teams to drive impact at scale. Curiosity about culture and how people form connections today. 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Mentor and educate ML Engineers on current and up and coming research, technologies and best practices of doing ML at scale. Ensure the ethical and responsible use of ML/AI and compliance with privacy regulations to protect user data Communicate effectively to audiences of various technical and non-technical backgrounds Strong programming skills: Proficiency in Python and ML libraries such as PyTorch Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning machine learning for user facing products. Experience with causal inference, uplift modeling, and interventional data collection is a plus. System design & architecture: Strong background in setting up and optimizing ML infrastructure, including containerization (Docker), orchestration (Kubernetes), and CI/CD workflows for ML (e.g., model versioning, automated testing). Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow, or Weights & Biases 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. 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.. Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes. 5+ years of experience, depending on education, as an MLE, with at least 2 years in a senior or staff-level role Previous experience in User Growth or Monetization 3+ years of experience designing and developing end-to-end, production grade ML systems 4+ years of experience working in a cloud environment such as GCP, AWS, Azure 3+ years of experience leading projects with at least 1 other team member through completion. A degree in computer science, engineering, or a related field or equivalent experience.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/29062284", "isPrivate": false}, {"id": 19938972, "name": "Senior Machine Learning Platform Engineer", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1761886899, "t_create": 1704997239, "ats_job_id": "f6d1cdb5-efcd-4c13-bd51-570568138794", "display_job_id": "f6d1cdb5-efcd-4c13-bd51-570568138794", "type": "ATS", "id_locale": "6203985222364177919-en", "job_description": "Own or contribute to technical designs for the feature platform, training platform, serving platform, and underlying operational infrastructure that provides incremental delivery and impact. 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Perform other job-related duties as assigned. 3+ 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.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/19938972", "isPrivate": false}, {"id": 29487638, "name": "Staff AI Engineer, Trust & Safety Operations", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1761886899, "t_create": 1748961320, "ats_job_id": "8e55b4e5-27a4-415c-850a-f3532032c975", "display_job_id": "R0004427", "type": "ATS", "id_locale": "R0004427-en", "job_description": "Own the technical roadmap for AI automation across Moderation, Appeals, and Support workflows, driving discovery and prioritization of high-impact AI automation opportunities while providing hands-on technical leadership from concept to production. Prototype agentic solutions using the latest platforms and frameworks and integrate them with existing internal and third-party tools and systems. Deliver reliable, scalable, and robust automations with the appropriate evaluations, guardrails, human oversight, and clear performance monitoring. Drive adoption by producing documentation, running hands-on training and enablement sessions for non-technical operators, and curating prompt libraries and playbooks that empower self-service iteration. Collaborate closely with Data Scientists, Data Engineers, Product Managers, Backend Engineers, and the AI Platform Team to ensure a comprehensive and coordinated approach to improving operational efficiency. Embed safety, privacy, auditability, and responsible-AI standards into every workflow in partnership with Legal and Security teams. Mentor and educate ML Engineers and Platform Engineers on new trends and research in AI/ML that can be applied to Trust & Safety initiatives to promote user safety and improve AI-powered products and workflows. Agentic & workflow-orchestration expertise: Proven ability to design, build, and operate multi-step LLM agents with modern coordination frameworks. Applied AI engineering & prompt craft: Deep Python skills plus hands-on experience integrating foundation models and crafting robust prompts and utilizing vector databases. Rapid prototyping & experimentation: Comfortable shipping quick proofs of concept, running A/B or shadow launches, and iterating based on data. Backend, data-systems & tool integration: Skilled at wiring external services and internal data into agent workflows through well-designed APIs and schemas. Human-in-the-loop system design: Able to blend automation with human oversight through clear escalation paths, review checkpoints, and moderator tooling. Operator enablement & training: Talent for translating technical workflows into clear, actionable training for non-technical teams and supporting their day-to-day adoption. Working through ambiguity: Proven skill thriving in high-ambiguity, fast-moving environments\u2014prioritizing effectively, adapting plans quickly, and delivering impact amid shifting requirements. 7+ years of software or machine-learning engineering experience, with a recent focus on AI-driven automation or agentic systems. 2+ years delivering solutions that combine automated decision support with human-in-the-loop review, ideally in Trust & Safety, customer support, or adjacent domains. 2+ years designing and tracking operational metrics that demonstrate ROI, accuracy, and user-experience improvements for automated workflows. 1+ years of hands-on work prototyping or operating agentic workflows (e.g., MCP, Agentspace, n8n) in real-world or open-source projects. 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