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Hinge is the dating app designed to be deleted
In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role
Most consumer apps treat your attention as the product, optimizing every recommendation and notification to keep you scrolling. Hinge made the opposite bet: our mission is to get people off the app, out on a date, and eventually to delete us entirely. That is not a tagline. It is the actual constraint every AI system we build has to solve against.
The Director of AI, Dating Outcomes owns the hardest version of that problem. You will lead the AI systems at the core of how Hinge connects people: the recommendation engine, the ranking models, and the generative capabilities that help users put their best selves forward. The goal is not engagement, it is outcomes: fewer ratings per exchange, higher quality matches, and more people finding the person they were looking for. Because Hinge does not sell ads or user data, being exceptional at this is our business model. Reporting to the VP of AI, you will set the long-term AI roadmap for one of the company's most influential product areas and build the teams that bring it to life. The problems sit at the intersection of human behavior, sparse real-world signals, and a two-sided ecosystem of people who are not interchangeable, a different kind of ML challenge than most people in this industry have had the chance to work on.
Responsibilities
- Strategy and Vision: Own the AI strategy for dating recommendations across the full journey, from first candidate to last date. Build the frameworks to reason rigorously through ecosystem tradeoffs where a win for one cohort reshapes another, and set a vision ambitious enough that the org wants to chase it.
- Cross-Functional and Industry Leadership: Partner closely with other product groups to integrate AI capabilities across the product and shape roadmap decisions you do not own. Communicate honestly with executive leadership on strategy, tradeoffs, and progress. Own budget and investment strategy for the area, and represent Hinge externally through writing, talks, and community.
- Technical Leadership: Steer real-time, personalized recommendation systems that reduce ratings per exchange and drive better matches. Bring Generative AI to dating outcomes in ways that are differentiated, authentic, and grounded in how people actually connect.
- Responsible AI: Hold the bar on scalability, resilience, and rigorous evaluation for both performance and bias.
- Team and Organizational Development: Structure and scale the team for both deep execution and long-horizon bets. Develop emerging leaders, invest in succession across IC and leadership tracks, and build a culture where rigor, inclusion, and performance reinforce one another.
- Execution Excellence: Drive predictable delivery, balancing urgent risk response with long-term platform investments. Establish scalable operating rhythms for planning, technical reviews, and incident learnings.
What We're Looking For
- 10 or more years across data science, applied ML, and software engineering, with 6 or more years in AI or engineering leadership defining strategy, not just managing execution.
- A track record of leading real-time, personalized recommendation systems in production, including the relevance, fairness, and scale tradeoffs that carry real consequences.
- Demonstrated success scaling ML or AI teams and developing technical leaders who carry the work over years, not a single launch.
- Deep technical fluency across ML, RL, Generative AI, and MLOps, with the communication skills and executive presence to represent AI strategy at the highest levels of the organization.
- A degree in Computer Science, Machine Learning, or a related quantitative field. A PhD is a plus, not a requirement.
What Will Set You Apart
- Experience building AI systems in a two-sided or constrained marketplace, where optimizing for one group shifts outcomes for another and the right answer is rarely obvious.
- A history of deploying Generative AI thoughtfully in user-facing experiences, not as a trend to follow.
- External presence through publishing, speaking, or community contribution that reflects genuine intellectual engagement with the field.
- Prior work in a domain where user outcomes are high-stakes and hard to measure, whether health, education, finance, or something comparably consequential.
$252,000 - $323,000 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 New York City. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting. #LI-SM1 As a member of our team, you’ll enjoy: 401(k) Matching: We match 100% of the first 10% of pre-tax 401(k) contributions you make, up to a maximum of $10,000 per year. Professional Growth: Get an annual Learning & Development stipend once you’ve been with us for three months. You also get free access to Udemy, an online learning and teaching marketplace with over 6000 courses, starting your first day. Parental Leave & Planning: When you become a new parent, you’re eligible for 100% paid parental leave (20 paid weeks for both birth and non-birth parents.) Fertility Support: You’ll get easy access to fertility care through Carrot, from basic treatments to fertility preservation. We also provide a stipend towards fertility preservation. You and your spouse/domestic partner are both eligible. Date Stipend: All Hinge employees receive a $100 monthly stipend for epic dates– Romantic or otherwise. Hinge Premium is also free for employees and their loved ones. ERGs: We have eight Employee Resource Groups (ERGs)—Asian, Unapologetic, Disability, LGBTQIA+, Raices, Women/Nonbinary, Parents —that hold regular meetings, host events, and provide dedicated support to the organization & its community. At Hinge, our core values are… Authenticity: We share, never hide, our words, actions and intentions. Courage: We embrace lofty goals and tough challenges. Empathy: We deeply consider the perspective of others. Diversity inspires innovation
Hinge is an equal-opportunity employer. We value diversity at our company and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We believe success is created by a diverse workforce of individuals with different ideas, strengths, interests, and cultural backgrounds. If you require reasonable accommodation to complete a job application, pre-employment testing, or a job interview or to otherwise participate in the hiring process, please let your Talent Acquisition partner know.
#Hinge .
Salary Context
This $252K-$323K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Meetic, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($287K) sits 36% above the category median. Disclosed range: $252K to $323K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Meetic AI Hiring
Meetic has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $220K - $323K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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