Senior Software Engineer, Tinder
Machine Learning Infrastructure
$190-246k
AWS
Docker
GCP
Python
Scala
Java
Kafka
Redis
Go
Terraform
Jenkins
Azure
Spark
Prometheus
Grafana
Flink
DynamoDB
Senior and Expert level
Los Angeles
More information about location
Office located in West Hollywood, California
Swipe-based dating app
Be an early applicant
Swipe-based dating app
1001+ employees
B2CRetailB2BSocialDatingAdvertising
Be an early applicant
$190-246k
AWS
Docker
GCP
Python
Scala
Java
Kafka
Redis
Go
Terraform
Jenkins
Azure
Spark
Prometheus
Grafana
Flink
DynamoDB
Senior and Expert level
Los Angeles
More information about location
Office located in West Hollywood, California
1001+ employees
B2CRetailB2BSocialDatingAdvertising
Company mission
To power and inspire real connections by making meeting easy and fun for every new generation of singles.
Role
Who you are
- MINIMUM REQUIREMENTS: Bachelor’s degree or its U.S. equivalent in Computer Science, Computer Engineering, or a related field, plus 5 years of professional experience as a Machine Learning Engineer, Site Reliability Engineer, or any occupation/position/job title performing ML infrastructure or backend software engineering
- In lieu of a Bachelor’s degree plus 5 years of experience, the employer will accept a Master’s degree or U.S. equivalent in Computer Science, Computer Engineering ,or related field, plus 3 years of professional experience as a Machine Learning Engineer, Site Reliability Engineer, or any occupation/position/job title performing ML infrastructure or backend software engineering
- Must also have experience in the following: 3 years of professional experience designing and implementing large-scale distributed ML platform systems, using big data technologies including Apache Spark, Apache Kafka, Apache Flink, or Databricks. 3 years of professional experience using multiple modern programming languages, including Python, Scala, Java, or Go, to develop ML platform systems, backend services, data processing jobs, and automation tools supporting the ML lifecycle.
- 2 years of professional experience working with modern cloud platforms (including AWS, Azure, or GCP) and utilizing infrastructure-as-code practices, containerization tools (Docker on managed orchestration platforms including Amazon EKS or Amazon ECS), and monitoring systems based on Prometheus metrics and Grafana dashboards, including experience operating services backed by a timeseries metrics store including Grafana Mimir
- 2 years of professional experience designing and building infrastructure for recommendation systems, moderation pipelines, or large language model (LLM) serving and deployment systems, including experience with modern ML serving frameworks including Ray Serve or Triton, and with LLM-serving
- 2 years of professional experience in large-scale database design and optimization, and data pipeline performance tuning to support efficient data access patterns for ML workflows, including working with analytical storage systems including Delta Lake or data warehouses, including Redis, ValKey or DynamoDB
- 1 year of professional experience leading technical initiatives across multiple engineering teams, including establishing platform ownership models, providing hands-on technical guidance, and driving adoption of shared ML infrastructure components including standardized GitOps pipelines, and modern model-serving platforms
- 1 years of professional experience designing and implementing CI/CD automation pipelines and GitOps practices for ML infrastructure, using tools including Terraform, Terragrunt, Helm, and internal GitOps systems (including Scaffold) together with continuous integration systems (including Jenkins or Buildkite) to manage deployment strategies including canary releases, bluegreen deployments, and zerodowntime migrations of backend services
What the job involves
- Design, build, and maintain scalable machine learning (ML) infrastructure to support experimentation, training, deployment, and monitoring of ML models processing large-scale datasets with hundreds of billions of data points
- Develop and maintain robust, scalable infrastructure platforms that support the needs of machine learning engineers across multiple business units
- Design, build, and maintain data processing and moderation pipelines that handle large data volumes and integrate with trust and safety workflows
- Deploy and manage production ML systems using internal deployment tools and optimize compute and storage resources to ensure reliability, scalability, and cost efficiency
- Design, develop, and maintain application programming interfaces (APIs), including REST, gRPC, and GraphQL, to support internal ML platform services and system integrations
- Oversee deployment, monitoring, and performance of ML systems using observability tools to ensure compliance with technical specifications and service-level objectives
- Develop and implement model evaluation, validation, and quality assurance processes, including A/B testing frameworks and automated evaluation systems, to ensure model accuracy, reliability, and performance
- Design, develop, and maintain scalable ML platform systems and data infrastructure using distributed data technologies, including Apache Spark, Kafka, Flink, and Databricks, to support global data processing and analytics needs
- Analyze ML infrastructure requirements across business units and design technical solutions within defined scalability, performance, and cost constraints
- Support technical design and implementation of ML lifecycle infrastructure, including model training, serving, monitoring, feature stores, and evaluation systems, with an emphasis on platform engineering and self-service capabilities
- Mentor and provide technical guidance to junior engineers on ML systems, backend systems, scalable data pipelines, production reliability, and deployment best practices
- Participate in hiring activities by conducting technical interviews and providing input on candidate evaluations
- Develop and maintain technical documentation, including system designs, operational guides, and internal knowledge bases
- Design and optimize recommendation systems and moderation data pipelines, applying best practices for data versioning, feature management, and model evaluation
- Implement and optimization of backend and ML services to ensure reproducibility, reliability, and operational stability
- Design and optimize large-scale data pipelines and database systems to support efficient data access patterns for ML workflows
- Collaborate with cross-functional teams, including software engineers, data engineers, and ML engineers, to support the development and deployment of ML-enabled product features
- Design and maintain infrastructure supporting large language model (LLM) workloads
- Analyze and resolve complex distributed systems issues affecting performance, scalability, reliability, and availability of high-traffic ML applications
- Research and evaluate emerging ML infrastructure technologies and conduct proof-of-concept implementations to support architectural and technology decisions
- Stay current with advances in ML infrastructure, distributed systems, and data engineering, and apply industry best practices to ongoing platform development
- Telecommuting may be permitted
- When not telecommuting must report to 8800 Sunset Blvd. West Hollywood, CA 90069. Up to 10% domestic travel for team meetings and on-site trainings
Our take
In today's society, online communication is rapidly replacing face-to-face interaction across various aspects of life, including professional, platonic, and even romantic relationships. Dating apps like Tinder, with their quick sign-ups and swipe-based interface, have revolutionised the way people connect.
Tinder's innovative approach has significantly reduced the social stigma associated with online dating. This, coupled with the global rise of smartphones, has created a strong tailwind for the app. However, Tinder faces new challenges from competitors like Hinge, Bumble, and Thursday, who all capitalise on similar "swipe-based" mechanics, potentially attracting users away from the platform.
To address this, Tinder is actively seeking ways to reinvent its core product. The company is exploring the development of "Tinderverse", a shared virtual reality experience. Additionally, it is testing "Tinder Coins", an in-app currency earned through positive user behavior, allowing access to premium features. These initiatives demonstrate Tinder's commitment to remaining at the forefront of the ever-evolving online dating landscape.
Steph
Company Specialist at Welcome to the Jungle
Insights
Company
Company benefits
- Mind & Body: Take care of yourself with benefits to keep you physically and mentally healthy.
- Company Perks: As winners of Comparably’s 2022 Best Company Perks and Benefits Award, we’re proud to say we provide perks that make a difference
- Life @ Tinder: We support each other— from informal check-ins to snacks + catered lunches at HQ to the flexibility to take time off when you need it
Company HQ
Sunset Strip, West Hollywood, CA
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