hinge jobs in Match Group

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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": 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": 1742792638, "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": 19920639, "name": "Senior Machine Learning Engineer, Growth", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1742792638, "t_create": 1704920293, "ats_job_id": "cc9bba44-65fd-43c4-bb19-2dd9a34f62c4", "display_job_id": "R0004220", "type": "ATS", "id_locale": "R0004220-en", "job_description": "Develop and deploy targeting and timing models that underlie a smart notifications system that guides and influences user behavior to improve dating outcomes. Improve user engagement with notifications by optimizing for relevancy and personalizing for responsiveness. Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters. Keep abreast of and bring to Hinge applicable cutting-edge research, technologies, and best practices in the ML/AI space. 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. Proficiency in languages like Python, Java, or C++ Domain expertise: Deep understanding of machine learning model architectures, DNN experience a big plus. Proven track record of building, debugging, and fine-tuning machine learning models for user facing products. System design & architecture: Experience training and deploying large scale ML models especially 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 Ray, KubeFlow, or Weights & Biases is a plus. 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.. 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. 4+ years of experience as an MLE, previous experience in User Growth or Monetization is preferred. 2+ years of experience working on a cloud environment such as GCP, AWS, or Azure. 2+ years of experience designing and developing online and production grade ML systems. 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/19920639", "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": 1742792638, "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": 1742792638, "t_create": 1739293554, "ats_job_id": "47f77d01-7ffb-45d2-b3f6-df71b6cb3eba", "display_job_id": "47f77d01-7ffb-45d2-b3f6-df71b6cb3eba", "type": "ATS", "id_locale": "1012337885616464429-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. 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).", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://join.matchgroupcareers.com/careers/job/27248394", "isPrivate": false}, {"id": 25720841, "name": "Senior Data Engineer, Dating Outcomes", "location": "New York, New York", "locations": ["New York, New York"], "hot": 0, "department": "Hinge", "business_unit": "Engineering", "t_update": 1742792638, "t_create": 1729281095, "ats_job_id": "0164e706-70ce-49c2-8332-83d7eb4ce607", "display_job_id": "R0004222", "type": "ATS", "id_locale": "R0004222-en", "job_description": "Design, build, and optimize data pipelines that support critical foundational, cross-cutting product services, empowering the product team to make informed decisions. Drive advancements in our Data Engineering stack by implementing containerization, refining data modeling techniques, and evolving ETL pipeline development to support emerging business needs and scalability requirements. Leverage your data modeling expertise to design, build, and maintain our analytics data warehouse that delivers reliable, high-quality datasets for reporting, analytics, and machine learning purposes, supporting both short-term insights and long-term strategic initiatives. Advocate for Data Engineering best practices within the team, championing their adoption and supporting the continuous improvement of the team's capabilities. Work with stakeholders across the organization to understand their data needs and translate these needs into actionable data solutions. 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 in data engineering, delivering data products from conception to delivery and supporting their underlying infrastructure. Strong technical background in building and optimizing data pipelines, with proficiency in Python, SQL, and data modeling for different types of data sources and business processes. Technical expertise in the following: Python, Go, Kubernetes, Docker, Terraform, CircleCI, GitHub Actions, Kafka, PubSub, Dataflow, Redshift, BigQuery, Databricks, dbt, Looker Experience with cloud environments such as GCP, AWS, or Azure Strong communication skills (written and verbal), with an ability to work collaboratively with cross-functional teams, including backend engineering, data science, and product teams. Effective analytical, troubleshooting, and problem-solving skills, ensuring data systems operate efficiently and meet business needs. Mentorship experience, with a focus on guiding junior data engineers and contributing to team growth through knowledge-sharing and code reviews. 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Partner with the TA team to facilitate pre-boarding and onboarding logistics for new hires, including scheduling onboarding sessions, account setup, and internal communications. Process HR transactions in Workday, such as job changes, promotions, and terminations, and ensure data integrity and accuracy through regular data audits Provide administrative and coordination support across key HR processes such as performance reviews, compensation cycles, and engagement surveys. Assist HR Business Partners with data entry, tracking, and reporting to support workforce planning and decision-making. Assist with offboarding processes, ensuring a seamless experience for departing employees. Support other HR administrative and operational work as needed, including document management, HR communications, and special projects. Support the continuous improvement of HR systems and processes, ensuring efficiency and user-friendliness. 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Kotlin Proficiency: Develop and continuously enhance your understanding of Kotlin fundamentals, applying them to create efficient and effective solutions. Android Expertise: Gain a thorough understanding of Android development principles and design patterns, applying them to build intuitive and user-friendly applications. Issue Resolution: Triage, debug, and effectively resolve software issues, ensuring the stability and performance of our application. Codebase Stewardship: Actively contribute to the long-term health and evolution of the codebase, implementing best practices and innovative solutions. Code Reviews: Regularly perform code reviews, providing constructive feedback to peers to foster a culture of excellence and continuous improvement. Active Team Participation: Engage actively in team ceremonies such as stand-ups, planning sessions, and retrospectives to ensure alignment and promote agile methodologies. Effective Communication: Maintain clear and positive communication with team members and other departments, supporting an empowering and inclusive team environment. Professional Android Development Experience: You have 3-5 FTE years of experience in Android development, demonstrating the ability to design and implement mobile applications effectively. Google Play Publication: You have experience publishing at least one application on the Google Play Store, showcasing your understanding of the deployment process and maintaining a production application. Proficiency in Kotlin and Android SDKs: You are proficient in Kotlin, which is strongly preferred, and have a good understanding of Android SDKs and frameworks. Experience with Java in the context of Android development is also beneficial. Solid Software Development Fundamentals: You possess a good foundation in software development principles and are capable of creating efficient and maintainable code. 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