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About This Role
Know where you belong!
Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire.
When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week.
The Role
Match Group runs some of the most-used apps in the world, including Tinder, Hinge, Match, OkCupid, and Plenty of Fish, for tens of millions of people in 170+ countries. That makes our legal work unusually interesting: privacy for some of the most personal data people share, trust and safety at global scale, AI features shipping into consumer products, and regulation that keeps moving.
This role sits at the intersection of legal practice and applied AI, closer to an in-house R&D function than a traditional legal team role. It's for a tech-forward builder, an engineer or product person, who can find where AI can do legal work that humans do today (or work humans can't do at scale), prototype and ship tools and agents to handle it, and iterate with lawyers in the loop until those tools are part of how the function runs.
This is about applying AI to the substance of legal work: the research, analysis, judgment calls, contract review, and regulatory tracking that lawyers do every day. It is not about optimizing existing processes or rolling out off-the-shelf legal tech. You'll build things that didn't exist before, prototype quickly, evaluate rigorously, and ship what works.
You'll serve brands across Match Group's portfolio (Tinder, Hinge, Match, OkCupid, Plenty of Fish, and others) and work with Legal leadership and attorneys as your domain experts and end users. You'll partner with engineering, product, and other technical teams where deeper infrastructure is needed. You'll have a mandate to pick where to start, prove value, and scale what works across brands.
Why This Role Matters
AI is changing what's possible in legal practice on a timeline measured in months, not years. This role is our bet that the right way to capture that is to put a dedicated builder inside Legal and have them ship.
For the right person, this is an open mandate: real problems across a portfolio of brands, lawyers and leadership who want to lean into AI, and the freedom to define what an AI-native legal function looks like at Match Group.
You'll build for one sophisticated in-house team, not for customers, so what you ship gets used, measured, and improved where you can see it. And the goal is to make our lawyers better at the work only they can do.
What You'll Do
Identify the highest-leverage AI opportunities
- Spend time inside Legal teams across Match Group to understand how work happens today: product counseling, marketing reviews, contract review and negotiation, and regulatory compliance. Focus on where AI can do meaningful legal work, not just where workflows could be tidier.
- Develop a clear point of view on what AI can do in a legal context today, what's coming next, and where lawyers must stay firmly in the loop.
- Build the case for which opportunities are worth investing in, based on impact, feasibility, and legal risk. Prioritize across brands and teams with competing needs.
Build, prototype, and ship AI tools and agents
- Design and build AI-powered tools (assistants, agents, retrieval systems, evaluators) that handle real legal work end-to-end or alongside attorneys.
- Connect these tools to the data and systems they need (document repositories, knowledge bases, internal sources), working within IT, Security, and Legal Ops-governed infrastructure, not around it.
- Treat shipping as the starting line: measure quality against human baselines, debug edge cases, tune prompts and pipelines, and iterate as models and the business change.
- Design tools to work across brands where the legal work is shared, and flexible enough to handle brand-specific differences in products, markets, and regulations.
Train, launch, and iterate with Legal teams
- Onboard attorneys and other end users to new tools and workflows, gather feedback, and make the tools useful, not just functional.
- Create documentation, examples, and attorney-facing training that teach both how to use AI tools and where their judgment must remain the decision-maker. This is distinct from Legal Ops training on legal department systems.
- Build evaluation harnesses and quality metrics (accuracy against human baselines, hallucination rates, coverage, latency) so you know when something is working and when it isn't.
- Coordinate with Legal Operations, IT, Security, and Privacy whenever a tool touches enterprise systems or governance. They own those layers, and anything that touches them needs their involvement.
Define what AI-native legal looks like at Match Group
- Partner with Legal leadership to develop a forward-looking view of an AI-native legal function and what it takes to get there.
- With Legal Operations, track the state of the art in legal AI and applied AI more broadly (what frontier labs are shipping, what legal-specific tooling is emerging, what other in-house teams are building) and translate it into what's worth trying here.
- Build the roadmap for how capabilities proven in one team or brand extend to others.
How Success Will Be Measured
Success here means novel legal capabilities created and adopted, not process or operational metrics.
- AI capabilities shipped that Legal teams actually use and rely on, not pilots that gather dust
- Speed and rigor of experimentation: how quickly and reliably you move from idea to evaluated prototype
- Categories of legal work meaningfully shifted from "humans only" to "AI-with-humans" or, where evidence supports it, AI-first with human audit”
- Adoption across Match Group Legal teams and brands
- Sustained quality: tools stay current, maintained, and performing as models and the legal landscape change
- Better use of Legal's attention: more attorney time on novel, high-judgment work, not just more output
Who You Are
- A builder at heart. You'd rather ship a rough prototype this week than write a perfect spec for one to be built next quarter.
- Equally comfortable talking with lawyers about risk and with engineers about data models, APIs, and configuration trade-offs.
- Hands-on with applied AI (prompting, agents, evaluations, RAG, fine-tuning) and curious about where models are headed.
- Pragmatic and business-oriented. You care less about technical purity than about shipping things that improve what Legal can do.
- Energized, not frustrated, by the fact that models, tools, and best practices will keep changing.
- Honest about what AI can and can't do today. You don't oversell it to lawyers or undersell it to skeptics.
- A curious, respectful change agent. You move fast, but you take time to understand existing frameworks, tooling, workflows, and the teams you're working with, across brands that each have their own way of doing things.
Qualifications
No law degree required. Strong candidates for this role come from engineering and product, and from legal backgrounds where they learned to build. Either way, you'll understand how lawyers think well enough to earn their trust.
Required
- 4+ years building, deploying, or applying AI products and tools at an AI-forward tech company, a startup, a law firm innovation team, or in-house, and 2+ years building hands-on with LLMs.
- Hands-on experience with prompting, agentic workflows, retrieval-augmented generation, evaluations, and the basics of fine-tuning or model selection
- Strong technical fluency: building working systems yourself (in code or with AI coding tools like Claude Code, Codex, or Cursor), working with APIs and data, and reasoning about integrations, permissions, and guardrails
- A track record of shipping to real users, learning from how they use it, and iterating
- Excellent communication across legal, technical, and business audiences. You're a teacher who can bridge technical and plain language.
Preferred
- Experience building AI products in regulated, compliance-sensitive, or high-stakes domains (legal, healthcare, financial services, trust & safety)
- Experience as a legal engineer, legal AI engineer, legal solutions architect, legal technologist, or applied AI engineer at a law firm, tech company, or AI lab
- Experience building something new within a defined team, measuring it rigorously, and making the case for broader rollout
- Experience supporting multiple business units or brands with shared tooling
$150,000 - $220,000 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 Los Angeles, CA.
The salary range for San Francisco, CA is $180,000-$220,000.
For all other locations, this salary may 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-LH1
Why Match Group?
Our mission is simple – to help people find love and happiness! We love our employees too and understand the importance of all life's milestones. Here are some of the benefits we are proud to offer:
Mind & Body– Medical, mental health, and wellness benefits to support your overall health and well-being Financial Wellness– Competitive compensation, 100% employer match on 401k contributions up to 10% (cap at $10,000), as well as an employee stock purchase program to help you feel supported in your financial security Unplug – Generous PTO and 14 paid holidays so you can unplug Career– Annual training allowance for professional development and ERG membership opportunities and events so you feel connected and empowered in your work Family – Families come in all shapes and sizes so we offer 20 weeks of 100% paid parental leave, fertility, adoption, and child care resources, as well as pet insurance and discounts Company Gatherings – We host company events where our employees get to know each other and build a sense of connection and belonging!
We are proud to be an equal opportunity employer and we value the rich dynamics that diversity brings to our company. We do not discriminate on the basis of race, religion, color, creed, national origin, ancestry, disability, marital status, age, sexual orientation, sex (including pregnancy and sexual harassment), gender identity or expression, uniformed service or veteran status, genetic information, or any other legally protected characteristic. Period.
If you require a reasonable accommodation to participate in the hiring process — such as during pre-employment testing or interviews — please indicate this by selecting “Yes” in the accommodation request field. We’ll reach out to discuss your needs if you're selected for the interview stage. #MG .
Salary Context
This $150K-$220K range is above the median 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $150K to $220K.
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 San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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.
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