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About This Role
AI \& TECHNOLOGY ASSOCIATE (Entry\-Level)
*The future of software is AI. We’re looking for people who already live there.*
Location: Remote — Anywhere in the USA (Las Vegas, NV strongly preferred)
Company: Seeking.com
Department: Technology (cross\-functional rotation across DEA, AI, Product, Development, and Data Science)
Type: Paid, Full\-Time
Compensation: $80,000 – $95,000 annually (based on experience and qualifications)
Duration: 6 to 12 months — with a clear path to a permanent role on the DEA, AI, Product, Development, or Data Science team
About Seeking
=================
Seeking.com is the world’s largest premium dating platform. Founded and led by an MIT alumnus, headquartered in Las Vegas, and operating as a hybrid\-remote organization, we sit at the intersection of artificial intelligence, software engineering, data science, and the psychology of human connection.
At Seeking, we believe the future of software is AI. We don’t treat AI as a feature bolted onto existing products. We treat it as the foundation of everything we build. Our codebase, our product experiences, our data pipelines, and our decision\-making infrastructure are all being reimagined around what AI makes possible. If you’re the kind of person who already uses AI every day — to write code, to analyze data, to prototype, to think — you already understand why we operate this way.
Beyond the platform itself, our ambition is larger. We are rethinking how social networks can be redesigned to help people build stronger, more positive relationships — with others and with themselves — with the goal of advancing humanity as a whole. The technology we build serves that mission.
The Role: Five Teams, One Launchpad
=======================================
This is not a traditional internship. This is not a job where you observe. This is a 6\-to\-12\-month entry\-level position designed to immerse you in the technical core of Seeking and help you discover which team is the right long\-term home for your talents and ambitions.
You will rotate through real work across five teams:
- DEA (Data Engineering \& Analytics) — the team that builds and maintains the data infrastructure powering every decision we make. Pipelines, warehouses, dashboards, and the analytics layer that turns raw data into business intelligence.
- AI — the team that researches, develops, and deploys AI/ML models across the platform. Recommendation systems, content moderation, matching algorithms, personalization engines, and experiments with emerging AI capabilities.
- Product — the team that defines what we build and why. Product strategy, user research, design, experimentation, and the end\-to\-end lifecycle from concept to launch.
- Development — the team that builds the platform itself. Front\-end, back\-end, mobile, APIs, and infrastructure. In our vision, software will increasingly be written, tested, and deployed with AI as a co\-pilot and eventually as the primary author. We need developers who can work in this new paradigm.
- Data Science — the team that turns data into understanding. Statistical modeling, experimentation design, behavioral analysis, and the deep quantitative work that powers our product and business strategy. Data scientists at Seeking don’t just crunch numbers — they uncover the patterns of human behavior that drive every decision we make.
The unifying thread across all five teams is AI. Every team at Seeking uses AI. Every team needs people who don’t just understand AI but who think with it, build with it, and push its boundaries daily. That’s what makes this role unique: no matter which team you ultimately join, your AI fluency will be your most valuable asset.
Who We’re Looking For
=========================
The right candidate for this role is rare: someone who combines deep technical curiosity with the drive and versatility to contribute across multiple disciplines. Specifically:
- You are AI\-native. This is the non\-negotiable. You don’t just “use” AI — you live in it. You’ve used tools like ChatGPT, Claude, Cursor, GitHub Copilot, Midjourney, or similar to write code, analyze data, generate designs, automate workflows, or build prototypes. You have a point of view on where AI is heading. You’ve probably built something with AI that nobody asked you to build. If someone has to explain to you why AI matters, this role is not for you.
- You are a software person at your core. You can code. You understand how software systems work — APIs, databases, front\-end and back\-end architecture, deployment. Whether your path leads to engineering, data, AI, or product, you speak the language of software fluently. You’ve built things — apps, scripts, tools, automations, models — and you can show them to us.
- You are relentlessly curious. You’re the person who signs up for every beta, reads every changelog, and has an opinion on every new tool before anyone else at your school or job has even heard of it. You investigate what’s next because you can’t help yourself.
- You think in systems. You don’t just see a feature — you see the data that feeds it, the model that powers it, the pipeline that delivers it, the product logic that triggers it, and the user behavior that validates it. You instinctively think about how the pieces connect.
- You have a builder’s mentality. You don’t wait for instructions. You see a problem, you prototype a solution, you test it. You have side projects, experiments, or personal tools you’ve built because you were curious. Your GitHub (or equivalent) has activity.
- You are hungry. You see this role as a launchpad into a career at the frontier of AI and technology. You want to grow into a data engineer, an AI/ML engineer, a product manager, a software developer, or a data scientist — and you’re willing to outwork and outlearn everyone around you to get there.
- You believe technology should serve humanity. You’re drawn to the idea that platforms like Seeking can be redesigned to help people build better relationships and better lives. You want your technical skills to mean something beyond the code.
What You’ll Do
==================
Your work will span real projects across all five teams. Depending on the rotation, expect to:
### Data Engineering \& Analytics (DEA)
- Build and maintain data pipelines that power analytics, reporting, and machine learning models
- Write SQL queries and build dashboards to surface insights for product, marketing, and leadership teams
- Work with data warehousing tools and ETL processes to ensure data integrity and accessibility
- Analyze user behavior and business metrics to identify patterns, anomalies, and opportunities
- Automate data workflows using Python, SQL, and AI\-assisted tooling
### Artificial Intelligence
- Assist in developing, training, and evaluating AI/ML models for recommendation, personalization, and content moderation
- Experiment with large language models, generative AI, and emerging AI frameworks
- Prototype AI\-powered features and present findings to the AI and product teams
- Evaluate new AI tools, platforms, and research for potential application to Seeking’s product
- Contribute to the team’s exploration of how AI can fundamentally improve how users connect on the platform
### Product
- Write product requirements, user stories, and acceptance criteria for features in development
- Conduct user research, competitive analysis, and market research to inform product decisions
- Design and run A/B experiments to optimize engagement, conversion, and retention
- Create wireframes and prototypes using Figma, code, or AI\-assisted design tools
- Collaborate with engineering, design, and QA to ship features end\-to\-end
### Development
- Write production code for features, services, and internal tools across the Seeking platform
- Use AI coding assistants (Cursor, Copilot, Claude) as part of your daily development workflow
- Participate in code reviews, architecture discussions, and technical design sessions
- Build and deploy microservices, APIs, and front\-end components
- Contribute to the team’s evolving practices around AI\-assisted software development — helping define how we build software in an AI\-first world
### Data Science
- Build statistical models to understand user behavior, predict outcomes, and inform product strategy
- Design and analyze experiments (A/B tests, multivariate tests) to measure feature impact with rigor
- Develop behavioral segmentation models that help us understand and serve different user populations
- Create data visualizations and presentations that translate complex findings into clear, actionable recommendations
- Collaborate with Product, AI, and DEA teams to ensure data science insights are embedded into every major decision
### Across All Teams
- Use AI tools daily to accelerate your work — research, prototyping, coding, analysis, and documentation
- Present your work, findings, and proposals to senior leadership regularly
- Identify inefficiencies and propose AI\-driven solutions to improve team productivity and product quality
- Contribute to Seeking’s broader vision of advancing how technology serves human relationships
Qualifications
==================
### Required
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, Mathematics, or a related technical field (recently completed or in final semester). We appreciate candidates with Ivy League pedigree, but we equally value those from any institution who have maintained a strong GPA and demonstrated they can excel academically.
- Demonstrated AI proficiency. You actively and fluently use AI tools (ChatGPT, Claude, Cursor, Copilot, Midjourney, or similar) to code, analyze, build, and think. You can demonstrate specific examples of how AI has accelerated your work or enabled you to build something you couldn’t have built without it. This is the single most important qualification.
- Strong software engineering fundamentals. Proficiency in at least one programming language (Python, JavaScript, TypeScript, or similar). Understanding of APIs, databases, version control (Git), and basic system architecture.
- Data fluency. Comfortable working with data — querying with SQL, analyzing datasets, interpreting metrics, and using data to inform decisions.
- Builder’s portfolio. You have built things — apps, tools, models, automations, prototypes, or side projects — and you can show them to us. A GitHub repo, a portfolio, a demo, a write\-up — something tangible.
- Strong analytical and problem\-solving skills — you think logically, debug methodically, and reason from first principles
- Excellent communication skills — you can explain complex technical concepts to both technical and non\-technical audiences
- Self\-directed and proactive — you don’t wait to be told what to do, you find problems and solve them
- Must be able to travel for team meetings, company events, and project needs.
- Strong preference for candidates open to relocating to Las Vegas, Nevada — our headquarters and technology hub.
### Preferred
- Experience with machine learning frameworks (TensorFlow, PyTorch, scikit\-learn) or LLM APIs (OpenAI, Anthropic, etc.)
- Experience building data pipelines or working with data warehousing tools (BigQuery, Snowflake, Redshift, or similar)
- Familiarity with cloud platforms (AWS, GCP, or Azure)
- Experience with product analytics tools (Mixpanel, Amplitude, Google Analytics)
- Familiarity with Agile/Scrum development methodologies
- Exposure to design tools (Figma, Framer) and prototyping workflows
- Coursework or self\-study in machine learning, natural language processing, computer vision, or behavioral science
- Experience with statistical modeling, hypothesis testing, or experimental design (R, Python statsmodels, or similar)
- Experience in dating, social networking, marketplace, or consumer subscription products
What We Offer
=================
- Competitive compensation: $80,000 – $95,000 annually — above the market average for entry\-level technical roles, because we’re looking for above\-average people.
- A real career path with five possible destinations. This position is designed to convert into a permanent role on the DEA, AI, Product, Development, or Data Science team. You’ll discover where you excel, and we’ll help you get there.
- AI is not a buzzword here — it’s how we work. You’ll use AI every single day, across every team. This isn’t a company that’s “exploring” AI. This is a company that’s building its future on it.
- Mentorship from senior engineers, data scientists, and product leaders who will invest in your growth and challenge you to think bigger.
- Meaningful work from day one. You’ll write production code, build real pipelines, ship features to millions of users, and prototype AI\-powered experiences. No busywork.
- A front\-row seat to the AI transformation of software. Our goal is a world where software is increasingly written by AI. You’ll be part of defining what that looks like in practice — not reading about it in a blog post.
- A mission worth building for. Seeking isn’t just a dating platform. We’re rethinking how technology can help people build stronger relationships and better lives. Your technical skills will serve a purpose bigger than the code.
- A culture that rewards builders, not talkers. We don’t reward attendance or activity. We reward people who ship, who solve, and who push the technology forward.
How to Apply
================
In your cover letter, answer these two questions:
1\. Tell us about something you built using AI. It doesn’t have to be polished or published. We want to understand how you think about AI as a tool, what problem you applied it to, and what you learned. Be specific.
2\. Of the five teams — DEA, AI, Product, Development, or Data Science — which one excites you most right now, and why? (It’s okay if you’re not sure. We want to see how you think about where your skills and curiosity intersect.)
Include links to your work: GitHub, portfolio, side projects, demos, blog posts, Kaggle notebooks — anything that shows us how you think and what you build.
*We’re not looking for someone who can pass a coding interview. We’re looking for someone who can’t stop building things.*
*Seeking.com is an equal opportunity employer.*
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Salary Context
This $80K-$95K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Reflex Media Inc, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($87K) sits 48% below the category median. Disclosed range: $80K to $95K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Reflex Media Inc AI Hiring
Reflex Media Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $95K - $95K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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