Interested in this AI/ML Engineer role at Tinder?
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About This Role
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. Salary: $190K \- $246K per year.
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.
CONTACT: Please email resume to: \[email protected]. Must specify Ad Code SLLL in subject line.
$190,000 \- $246,000 a year
Factors such as scope and responsibilities of the position, candidate's work experience, education/training, job\-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary range is reflective of a position based in West Hollywood, CA.
\#LI\-DNI
\#BI\-DNI
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Salary Context
This $190K-$246K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tinder, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $190K to $246K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Tinder AI Hiring
Tinder has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Hollywood, CA, US. Compensation range: $246K - $246K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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