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Spearhead the development and execution of automated tests (e.g., functional, regression, integration, and performance) to ensure efficient, repeatable, and reliable testing processes. Cross-Functional Collaboration: Work closely with product managers, developers, and UX teams to identify features and areas that will benefit most from automation. Provide technical input on requirements and user stories, advocating for automation solutions that enhance test coverage and reduce manual testing efforts. Bug Tracking & Resolution via Automation: Collaborate with development teams to integrate automated regression testing into the defect resolution workflow, ensuring long-term quality improvements. User-Reported Issue Integration: Incorporate user-reported issues into automated regression cases for continuous validation, helping to establish quantitative metrics for assessing the quality and reliability of the Hinge app. Develop automation scenarios that replicate real-world usage based on user feedback to ensure comprehensive test coverage. Process Improvement & Innovation: Continuously assess and refine the automation framework, testing processes, and methodologies to drive efficiency and improve overall product quality. Extensive QA Experience: Minimum of 5+ years in QA, with at least 3 years dedicated to automated testing of mobile applications in fast-paced Agile environments. Automation Expertise: Proven hands-on experience in designing, developing, and maintaining automated test frameworks for mobile apps using industry-standard tools such as Appium, Espresso, or XCUITest. Demonstrated ability to write robust, reusable test scripts in programming languages (e.g., Java, Kotlin, Swift) and integrate them within continuous integration pipelines (e.g., Jenkins, GitLab C, CircleCII). Deep Technical Proficiency: Advanced understanding of the Software Development Life Cycle (SDLC) and modern testing methodologies, with a track record of identifying, documenting, and prioritizing critical defects in a systematic manner. Expertise in test case design, execution, and automation, ensuring rigorous verification of product quality at every stage of the development process. Analytical & Problem-Solving Skills: Exceptional attention to detail combined with strong analytical abilities to thoroughly investigate, diagnose, and resolve complex issues, while proactively proposing innovative solutions. Proven experience in using data-driven approaches to enhance test coverage and quality, with a track record of recommending actionable improvements to optimize testing processes and product performance. Effective Communication: Excellent written and verbal communication skills, with a demonstrated ability to clearly articulate technical issues to both technical and non-technical stakeholders in a timely manner. Experience in preparing comprehensive test documentation, reports, and dashboards that drive decision-making. Quality and Performance Commitment: A relentless focus on delivering high-quality products, with an unwavering commitment to upholding the performance and integrity of the mobile application. Must demonstrate an ability to work under pressure and meet tight deadlines without compromising on quality. Cross-Platform Testing Proficiency: Strong background in testing both Android and iOS applications, with the capability to design cross-functional test strategies that ensure consistency and performance across platforms. Desirable but not Mandatory: Experience with AI-driven automation tools to leverage machine learning for predictive testing, adaptive test case generation, and enhanced defect detection is an added advantage. Experience Establishing and maintaining a robust automation framework from scratch, including integration with continuous integration/continuous deployment (CI/CD) pipelines.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/25880385", "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": 1745557290, "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. 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": 1745557290, "t_create": 1741811827, "ats_job_id": "9ae78f81-e838-4116-9852-07c6c5a6a051", "display_job_id": "9ae78f81-e838-4116-9852-07c6c5a6a051", "type": "ATS", "id_locale": "791336523376867373-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": 1745557290, "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": 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": 1745557290, "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": 27320603, "name": "Director of Data Engineering", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1745557290, "t_create": 1739999583, "ats_job_id": "15237b37-218a-434a-8c1a-121a13292182", "display_job_id": "15237b37-218a-434a-8c1a-121a13292182", "type": "ATS", "id_locale": "8996247942820886652-en", "job_description": "Play a key role on the Data Leadership Team, shaping Hinge's overall data strategy while setting and executing the Data Engineering strategy. Ensure strategic alignment with Hinge's broader business, product, and technology objectives, driving data-driven innovation across the company. Enable Hinge's AI and machine learning initiatives by building and optimizing high-quality, scalable, and reliable data pipelines. Transform the current existing data engineering practices into unified and streamlined standards that enable optimized, high quality, scalable, and reliable data pipelines. Lead, scale, and mentor a high-performing team of data engineers and data engineering managers, fostering a culture of growth, collaboration, innovation, and operational excellence. Drive technical excellence, upholding high standards for code quality, system design, scalability, and reliability across all data engineering initiatives. Provide hands-on technical leadership, actively participating in architecture reviews, design discussions, and guiding critical technical decisions. Collaborate cross-functionally with BI Engineers, Backend and Client Engineers, ML Engineers, Data Scientists, and Product Managers to develop and implement scalable, data-driven solutions that power analytics, experimentation, and machine learning. Ensure seamless data integration across Hinge's products, features, and systems, enabling a unified, high-quality data ecosystem. Define and implement best practices that enhance team efficiency, optimize workflows, and drive continuous improvement in data engineering operations. Stay ahead of industry trends and emerging technologies, driving innovation in data engineering to support Hinge's evolving business needs. 10+ years of experience in Data Engineering or a related field, with at least 4 years in engineering management leading high-performing teams. Demonstrated expertise in developing and maintaining scalable, reliable, and high-quality data products and pipelines that support business-critical analytics, experimentation, and AI/ML workloads. Experienced at building and managing data pipelines that hydrate online ML features. Deep expertise with modern data engineering technologies, including Kafka, Spark, dbt, Airflow, Databricks, and cloud-based data platforms (AWS, GCP, or Azure). Strong familiarity with modern cloud-native architectures and infrastructure, ensuring efficient, secure, and cost-effective data solutions. Strategic technical leadership with a proven ability to align data engineering strategies with product and business objectives, driving initiatives that exceed business goals. Passion for developing, coaching, and scaling high-performing teams, fostering a culture of growth, inclusivity, and operational excellence. Exceptional communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders, ensuring clarity and alignment across teams. Highly motivated, proactive and collaborative team player with the ability to persuade others and influence outcomes.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/27320603", "isPrivate": false}, {"id": 27248394, "name": "Senior Machine Learning Engineer, Trust & Safety", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1745557290, "t_create": 1739293554, "ats_job_id": "47f77d01-7ffb-45d2-b3f6-df71b6cb3eba", "display_job_id": "R0004257", "type": "ATS", "id_locale": "R0004257-en", "job_description": "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. 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. 4+ years of experience, depending on education, as an MLE or data scientist. 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Contribute to the research and development of recommender models as well experiment with the latest ML innovations (e.g. LLM agents and transcription models) Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters 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 on current and up and coming research, technologies and best practices of doing ML at scale. Perform other job-related duties as assigned. Strong programming skills: Proficiency in languages like Python, Java or C++ System design & architecture: Proven track record of training and deploying large scale ML models specially DNNs. Good understanding of distributed computing for learning and inference. Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow or W&B is a plus. ML knowledge: Deep understanding of DNN architectures, track record of building, debugging and fine tuning models. Familiarity with PyTorch, TF, knowledge distillation, recommender systems are a plus. Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure such as Kubenetes and Terraform. Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Deep 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.. 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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. Cross-functional Collaboration: Work closely with internal teams (Policy, Legal, Product, Engineering, and Operations) to align QA initiatives with business goals and regulatory requirements. Vendor & Partner Management: Oversee external QA vendors, ensuring adherence to quality standards and driving continuous process enhancements. Experience: 3+ years in quality assurance, content moderation, trust & safety operations, or a related field, preferably within a scaled operations environment. Industry Knowledge: Deep understanding of Trust & Safety, content moderation, and risk management processes, including regulatory compliance requirements. Analytical & Problem-Solving Skills: Strong data analysis capabilities, with experience leveraging QA tools, dashboards, and statistical insights to drive decision-making. Process Improvement Expertise: Proven ability to design, implement, and optimize QA frameworks and moderation workflows in high-volume operational settings. 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