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About This Role
DIRECTOR, AI & DATA SCIENCE
Data Engineering & Analytics, AI, and Data Science | Full-Time, Salaried | Player-Coach | Reports to the Co-CEOs
Company
Seeking.com (Reflex Media)
Location
Remote, anywhere in the USA
Team
Six to seven individual contributors across data engineering, analytics, data science, ML, and MLOps
Education
Bachelor’s degree in a technical field required. Master’s or PhD preferred.
Compensation
$175,000 to $225,000 base
Schedule
Collaboration across time zones. Regular working-hour overlap with our Singapore Dev team and partners is required.
Travel
Required for team weeks and quarterly planning in Las Vegas
About the Role
Seeking.com is the world's largest premium dating platform, founded and led by an MIT alumnus and headquartered in Las Vegas. We are a lean company of about 60 people, so every leader here builds.
You will lead our combined Data Engineering & Analytics, AI, and Data Science team of six to seven individual contributors and report directly to the Co-CEOs. This team is Seeking's central AI capability: the models, infrastructure, evaluation frameworks, and continuous-learning systems the whole company runs on. Expect about half your time leading and half writing code, building models, and debugging production systems yourself.
Your first mandate is to achieve 100% AI automation of routine user-uploaded content moderation, with accuracy proven superior to our human moderators and at a lower total cost. Next is AI customer support that resolves member tickets end to end. You own both outcomes completely, from policy and data through production results.
Over time, the team will extend AI to fraud prevention, matching, member onboarding and success, marketing, monetization, retention, and product development, in partnership with the leaders who own those areas. Every initiative carries a measurable target, a named owner, and a mechanism for continuous learning. The immediate problems come first, and this role exists to solve them.
This role is for someone who manages a hands-on team today and still ships code. If your recent roles had managers reporting to you, or you plan to delegate the technical work and manage through status meetings, this is the wrong role. If you walk into ambiguity and know exactly what to do, keep reading.
What You Will Do
1. Set the foundation in your first 30 days
- Bring Product, Support, and Operations to one written moderation policy with a single owner. When teams disagree, you run the process that settles it.
- Inventory every production AI system with a named owner, backup, dependencies, and monitoring gaps.
- Trace every upload from receipt through model decision, enforcement, and any human review. Quantify every path that still needs a human, with its root cause, cost, and fix.
2. Prove AI beats our human moderators, then reach 100%
- Test the AI and our moderators on the same held-out cases, with reference decisions set by independent, policy-qualified reviewers. Report missed violations and wrongful rejections separately, with sample sizes and confidence intervals.
- Close every gap: missing coverage, uncertain model outputs, system failures, unenforced decisions, and duplicate human review. Expand automation in controlled stages, each with a tested rollback.
3. Build systems that keep getting better
- Every production system improves through verified feedback, independent evaluation, and controlled releases. Run a monthly learning cycle in which a new model ships only when it beats production on the same independent test.
- Version every dataset, policy, prompt, and model so every gain can be proven and every change can be rolled back.
4. Run production AI like a business
- Give every production AI system monitoring, alerts to a named owner, documented recovery, and regular failure drills.
- Report results in business terms: quality, cost per outcome, revenue, efficiency, and member experience, with savings reconciled with Finance. Keep one executive dashboard and run a weekly review with the Co-CEOs.
5. Automate support, then partner across Seeking
- Own AI customer support end to end: agents that resolve tickets and execute approved actions, such as subscription changes and eligible refunds, held to the same evaluation, rollout, and monitoring standards as moderation.
- Once moderation and support are delivered, partner with the leaders of Marketing, Product, and other departments on fraud, matching, onboarding, monetization, and retention. Bring the Co-CEOs a ranked list of opportunities by impact on cost, revenue, efficiency, and member experience.
- In those partnerships, department leaders own their business outcomes. Your team owns the models, infrastructure, evaluation, and learning systems, and is accountable for their performance.
6. Lead and grow the team
- Review each person's work line by line, pair on hard problems, and give direct feedback every week. Build a growth plan for everyone, recruit strong talent, and address performance gaps early.
- Set quarterly OKRs for the function and each direct report, with leading and lagging KPIs for every Key Result. Run the work in Jira, Confluence, and Monday.
What You Must Bring
- A Bachelor’s in Data Science, Computer Science, Software Engineering, or a closely related technical field. A Master’s or Ph.D. in CS, AI, ML, Statistics, or a related quantitative field is strongly preferred.
- Seven or more years delivering production data or ML systems, including three or more years managing teams of individual contributors. You manage a hands-on team today, or did so recently.
- Strong Python and SQL, with production code you personally wrote in the past year. You will code in the interview.
- Production ML and architecture judgment. You have designed, built, and operated ML and LLM systems end to end, from data pipelines through evaluation, deployment, and monitoring, on a major cloud platform.
- You have done this before. You led an AI automation program in moderation, trust and safety, support, or a comparable decision system, from a measured human baseline to automated decisions at scale.
- Evaluation rigor and continuous learning. You measure false negatives and false positives separately, report uncertainty, keep test data clean, and have built systems that improve through feedback and controlled updates.
- Measurable business impact. You can name the cost, revenue, efficiency, or member outcomes your work moved, with the numbers.
- Cross-functional influence. You have found AI opportunities in other departments, won over their leaders, and taken the solution from concept into production.
- A record of mentoring engineers and scientists into stronger, more senior contributors.
- Daily use of Claude, Claude Code, Cowork, or comparable AI tools. If someone has to explain to you why AI matters, this role is not for you.
Nice to Have
- Production work in multi-modal or computer vision moderation, fraud detection, recommender systems, or marketing and monetization models such as propensity, pricing, and lifetime value.
- Our stack: AWS (S3, Redshift, SageMaker, Bedrock), Looker, and Kubernetes.
Compensation
- $175,000 to $225,000 base, commensurate with experience. Full-time, salaried, remote within the US.
- 99% coverage of our medical base plan, dental, and vision. 50% 401(k) match up to 6%. Flexible Spending Account and flexible paid time off.
How We Hire
- Recruiter screen: code you wrote in the last 90 days, the team you manage, and logistics.
- Practical round: live SQL and Python, plus a production architecture discussion.
- Systems round: work through a simplified moderation workflow. Find where items fall back to humans, define an honest automation rate, and design the evaluation against human moderators.
- Co-CEO round: treat it as a pitch. Study the product, ask sharp questions, and bring your 90-day plan for moderation and customer support.
- References, including a former direct report.
Equal Opportunity Employer
Reflex Media is an equal opportunity employer committed to diversity and inclusion in the workplace. We strictly prohibit discrimination of any kind. Candidates are encouraged to apply for qualified positions regardless of race, color, sex, religion, sexual orientation, national origin, disability, genetic information, or any other protected characteristics as outlined by federal, state, or local laws. Reflex Media's hiring decisions are based solely on qualifications, merit, and the needs of the company. All offers of employment are contingent upon the completion of a full background and reference check.
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Salary Context
This $175K-$225K 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 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 $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 ($200K) sits 6% below the category median. Disclosed range: $175K to $225K.
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.
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: $225K - $225K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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