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Develop, maintain, and enhance reusable frameworks for AI/ML model development and deployment while establishing and driving best practices in machine learning engineering and MLOps. Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering incrementally. Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process. Mentor and educate ML Engineers and Data Scientists on current and up and coming tools and technologies for ML operations through presentations and documentation. Help design and architect an AI platform that adheres to the principles of responsible AI and simplifies privacy compliance. Lead build vs buy discussions on technologies that would underpin the feature, training, and serving layers. 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": 27719252, "name": "Staff Machine Learning Platform Engineer", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1749531705, "t_create": 1741811827, "ats_job_id": "9ae78f81-e838-4116-9852-07c6c5a6a051", "display_job_id": "R0004338", "type": "ATS", "id_locale": "R0004338-en", "job_description": "Define the long-term, holistic roadmap for the Feature Store platform, aligning it with company-wide ML initiatives and ensuring end-to-end integration with model training, serving and observability platforms. Evaluate and introduce new technologies, tools and best practices that enhance feature serving reliability, scalability, cost efficiency and throughput, including leading build vs buy discussions. Architect, build, and maintain frameworks enabling MLEs for self service data ingestion and serving pipelines for both offline (batch, async) and online (low-latency) feature stores. Partner with cross-functional Platform teams to represent feature engineering requirements and incorporate them into Hinge's wider Platform capabilities. Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand the ML development lifecycle and identify opportunities to accelerate the AI/ML development and deployment process. Mentor and educate ML Engineers and Data Scientists on current and up and coming methods, tools and technologies for Feature Engineering. Help design and architect an AI platform that adheres to the principles of responsible AI and simplifies privacy compliance. 5+ years of experience, depending on education, as an ML Platform Engineer, Data Engineer, or Platform Engineer developing and working with large scale, complex data processing and or warehousing systems. 4+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes 3+ years of experience leading projects with at least 2 other team members through completion. 2+ years of experience for Staff designing and developing online and production grade ML Feature Store 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, particularly data intensive systems. Data engineering expertise: Skills in handling and managing large streaming data processing systems and formats (parquet, json, protobuf, delta) including data cleaning, preprocessing and storage systems. Feature Store Platform technology skills: The ability to establish and use Feature Store platforms such as Databricks, Feast, Tecton, Hopsworks, Ray, and/or similar. Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. ML knowledge: Broad awareness of the entire ML lifecycle, including the data needs for training, serving and evaluation. Communication skills: The ability to communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds through documentation, RFCs and presentations. Software leadership skills: A track record of leading projects with multiple contributors and stakeholders through completion with quantifiable and measurable outcomes. Strategic leadership skills: Demonstrated technical leadership experience in aligning platform strategy with product and business objectives.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/27719252", "isPrivate": false}, {"id": 28194763, "name": "Senior Policy Manager", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Trust & Safety", "t_update": 1749531705, "t_create": 1744140655, "ats_job_id": "c9dd380c-eb08-43fe-b706-6d691ace7309", "display_job_id": "R0004273", "type": "ATS", "id_locale": "R0004273-en", "job_description": "Translate high-level platform and regulatory requirements into clear, scalable content and safety policies. Ensure effective implementation of policies across moderation, product, and operations. Lead development of enforcement guidance and documentation aligned with internal tooling. Contribute to responsible AI governance, ensuring automated enforcement aligns with platform principles and mitigates risk. Future potential to manage and coach a small team, balancing individual contributions with people leadership. In near term, serve more as a peer mentor for more junior team members and take the lead on all things policy Coordinate the full policy lifecycle across internal and parent company stakeholders\u2014from drafting to rollout to iteration. Use data and enforcement trends to assess policy performance and proactively adapt approaches. Facilitate cross-functional planning and training to support adoption, clarity, and operational excellence. 6+ years of experience in policy development, trust & safety, legal, compliance, or platform governance. Experience managing or mentoring others in player-coach or team lead roles. Deep expertise in policy writing or implementation, operational scaling, and system design for policy enforcement. Proficiency in data analysis and risk evaluation, especially using enforcement and system-level data. Familiarity with AI-driven enforcement risks such as false positives, bias, and transparency challenges. Excellent communication and stakeholder management across technical, legal, and product audiences. Bachelor's degree or equivalent experience. Experience in Trust & Safety policy for an online platform. Knowledge of platform regulations (e.g., DSA, GDPR, transparency laws). Background in law, public policy, operations, or compliance. Experience scaling policy systems across global or third-party teams. Master's degree or relevant certifications a plus.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/28194763", "isPrivate": false}, {"id": 29062284, "name": "Staff Machine Learning Engineer, Growth", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1749531705, "t_create": 1746481441, "ats_job_id": "9638198d-e429-4945-802c-9c470e1ac692", "display_job_id": "9638198d-e429-4945-802c-9c470e1ac692", "type": "ATS", "id_locale": "4808491044942726365-en", "job_description": "Lead the end-to-end development of production-grade ML systems such as user targeting models that will help drive engagement, improve dating outcomes and/or improve user adoption of and engagement with paid features Define and own the technical roadmap for ML within your product area and align with company-wide priorities Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally Keep abreast of and bring to Hinge applicable cutting-edge research, technologies, and best practices in the ML/AI space. 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": 28253880, "name": "Lead Policy Manager", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Trust & Safety", "t_update": 1749531705, "t_create": 1744381067, "ats_job_id": "3137a4cf-128c-4387-83d6-85773f5d81cc", "display_job_id": "3137a4cf-128c-4387-83d6-85773f5d81cc", "type": "ATS", "id_locale": "1287650577084261081-en", "job_description": "Lead translation of high-level platform policies into clear, localized policies and enforcement guidance, owning end-to-end development that reflects legal, ethical, and user trust considerations\u2014anchored in Hinge's principles and product ecosystem Define and own Hinge's approach to scalable enforcement guidance, partnering with Engineering, Product, Legal, and Ops to ensure readiness across all surfaces and systems. Contribute to responsible AI governance, ensuring automated enforcement aligns with platform principles and mitigates risk. Operate as the primary Hinge liaison to our parent company on policy development and implementation, anticipating upstream changes and shaping platform-level direction on emerging policy areas. Analyze enforcement, product, and market data to guide policy and enforcement guidance refinement and proactively flag risk areas or emerging integrity challenges. Serve as a mentor (future potential to manage and coach) and strategic resource to policy peers, helping shape team-wide workflows, decision frameworks, and quality standards. Translate complex regulatory frameworks, platform guidance, and ethical questions into actionable decisions that scale across manual and automated moderation systems. 8+ years of experience in trust & safety, platform policy, legal, compliance, or regulatory advisory roles\u2014ideally within a high-scale consumer technology environment. Proven track record leading policy design and/or implementation across cross-functional teams and/or platforms. Deep understanding of the global digital policy landscape, with the ability to translate abstract legal or ethical frameworks into user-appropriate guidance. Experience operationalizing policy across a variety of enforcement systems, including those using machine learning or automation. Strong data fluency: comfortable using enforcement, user behavior, or legal trend data to inform decisions and drive policy iteration. Demonstrated ability to lead through influence\u2014aligning teams and executives around principled, strategic decisions in ambiguous contexts. Exceptional written and verbal communication skills; adept at driving clarity in high-stakes conversations across diverse audiences.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/28253880", "isPrivate": false}, {"id": 25720841, "name": "Senior Data Engineer, Trust & Safety", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1749595941, "t_create": 1729281095, "ats_job_id": "0164e706-70ce-49c2-8332-83d7eb4ce607", "display_job_id": "R0004272", "type": "ATS", "id_locale": "R0004272-en", "job_description": "Work with our data teams to ensure data is flowing accurately through data creation to our presentation layers. Improve our Data Engineering stack through containerization, data modeling, developing our ETL pipelines, and more. Use your expertise in data modeling to design, build, and maintain our analytics data warehouse, which provides clean, accurate, and robust data sets that can be leveraged for reporting, analytics, and machine learning initiatives. Work with stakeholders and translate their needs and expectations into action items and deliverables. Continue to learn more about the Data Engineering discipline, utilize that knowledge in your deliverables, and identify opportunities to enhance our pipelines. Participate in our on-call rotation to ensure system reliability, promptly addressing issues to ensure smooth operations. 5+ years of professional/industry experience Proficient in Python, SQL, DevOps, and databases. 5+ years of professional/industry experience. Experience delivering data products from conception to delivery and with the infrastructure that supports their underlying processes. Experience modeling data sets for different types of sources and business processes. Passionate about designing elegant data infrastructure, tooling, and pipelines. Familiarity with our stack: Kubernetes, Docker, Terraform, Kafka, Airflow, dbt, Looker, AWS technologies (S3, Redshift), GCP technologies (Dataflow, BigQuery). and CI/CD technologies (CircleCI). Spark/Databricks a plus!", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/25720841", "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": 1749531705, "t_create": 1748961320, "ats_job_id": "8e55b4e5-27a4-415c-850a-f3532032c975", "display_job_id": "8e55b4e5-27a4-415c-850a-f3532032c975", "type": "ATS", "id_locale": "2379892612527328112-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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Performance Monitoring & Insights: Establish key performance indicators (KPIs) and quality metrics to measure agent performance, decision accuracy, and process effectiveness. Process Optimization: Identify gaps in moderation workflows and recommend continuous improvement initiatives through automation, training enhancements, and best practice adoption. Training & Calibration: Partner with policy, training, and operations teams to create robust calibration processes, ensuring alignment between human moderators, AI models, and policy guidelines. Incident Management & Root Cause Analysis: Investigate quality-related issues, conduct root cause analysis, and implement corrective actions to mitigate risks and prevent recurrence. Data-Driven Decision Making: Analyze QA data and generate actionable insights to improve moderation outcomes, agent performance, and overall operational efficiency. 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