Interested in this AI/ML Engineer role at GR0?
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Search is being rewritten. Come lead the team doing it.
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The way people find brands is changing faster than it ever has, and most agencies are scrambling to keep up. GR0 isn't.
We're one of the fastest\-growing digital marketing agencies in the country, and we're hiring a Director of SEO \& AI Search to own the evolution of our core service line. This is the role for someone who sees what's coming in search, has the chops to build for it, and wants to lead a team doing work that actually matters.
You'll have a real seat at the table. You'll own outcomes, not just tasks. And you'll have the resources, the team, and the client portfolio to prove what's possible when deep SEO expertise meets genuine AI fluency.
What this role actually is:
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You're the head of SEO at a high\-growth agency working with some of the most ambitious D2C brands in e\-commerce. Your job is to make the department perform, scale, and stay ahead.
That means leading a talented team of SEO managers, driving retention and results across a diverse client portfolio, and being the person in the room who knows both the technical fundamentals and where the industry is going next.
You'll also be a builder. New processes, new capabilities, new revenue streams. If something isn't working, you fix it. If there's an opportunity no one has spotted yet, you find it.
What you'll own:
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The team. Hire great people. Coach them well. Build a culture where high standards and psychological safety coexist. Weekly 1:1s are coaching sessions, not check\-ins.
The clients. Senior voice on strategy calls, QBRs, and escalations. You know which accounts are at risk before they churn, and you have a plan ready before anyone asks.
The work. Full\-funnel SEO strategies built on data: technical audits, content roadmaps, backlinking, digital PR, and cross\-platform approaches spanning Google, Bing, Amazon, and beyond.
The business. Department P\&L, capacity planning, tooling decisions, sales pitches. You're not just running a service line. You're running a business unit.
The future. AI search is not a side project. You'll lead GR0's point of view on generative engine optimization and make sure the team is ahead of every platform shift, not reacting to it.
Who we're looking for:
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You have 5\+ years of hands\-on SEO experience, including 2\+ years managing people, with agency experience that's non\-negotiable. You've managed managers. You've owned a P\&L. You've recovered at\-risk campaigns and helped close new business.
But beyond the resume, you're the kind of leader who gets energy from building. You're curious about AI search, not intimidated by it. You know how to win over a C\-suite client and how to give a junior employee feedback they'll actually use. You hold a high bar and people still want to work for you.
### Required:
- 5\+ years of hands\-on SEO experience, 2\+ years in people management
- Agency experience (required, not preferred)
- Proven track record managing managers and leading high\-performing teams, including offshore
- Deep expertise across technical SEO, content, and off\-site disciplines
- Financial acumen with P\&L ownership experience
- Genuine enthusiasm for AI's role in search and the future of discovery
- Strong executive presence: comfortable on pitch calls, client calls, and the conference stage
### Preferred:
- Experience with D2C brands and performance marketing environments
- Prior success managing offshore talent across time zones
- Entrepreneurial background or history of launching new initiatives inside an agency or startup
### What winning looks like here?
Team members promoted from within. 1:1s never skipped. SOPs that the team actually uses.
And a department that's recognized, inside the agency and outside it, as the standard for what SEO looks like in the age of AI.
### Hiring Process:
- Stage 1 — Recruiter Screen After submitting your application, our People \& Culture team will review and reach out to selected candidates. During this 30\-minute call, we'll get to know your career journey, professional motivators, and what excites you about GR0 — while giving you a full picture of who we are and what we're building.
- Stage 2 — Panel Interview You'll meet with members of our leadership team for a deeper dive into your background and experience. Expect to walk through your technical skills, people management approach, and how you think about strategy — and of course, share why you're excited about this opportunity at GR0\.
- Stage 3 — Co\-Founders Connect The final conversation is always our favorite. You'll get to connect directly with our CEO \& Co\-Founder, Kevin Miller, and our Chief Growth Officer \& Co\-Founder, Jon Zacharias. This is your chance to ask big questions, share your vision, and see if the feeling is mutual.
- Stage 4 — Offer If a \#GR0mance has brewed — we'll extend a competitive offer and hope you'll join the team!
### What's in it for you?
- Shared Financial Success \- GR0 believes strongly in equal pay for equal work, and transparency in compensation is one vehicle to achieve that. Annual compensation for this role is market competitive.
- Health and Family Benefits \- GR0 offers comprehensive medical, dental, and vision coverage options for GR0ers and their families.
- Wi\-Fi Reimbursement \- All employees receive a monthly reimbursement to help cover internet expenses while working.
- Unlimited PTO \-We know that rest is vital to your well\-being and career success, so we give you the flexibility and trust to take it when you need it.
- Paid Holidays \- Take time to celebrate and rejuvenate with 13 fully paid holidays throughout the year.
- Community Impact \-GR0 is committed to supporting the communities where we work and live by partnering with local charitable organizations to give back in various ways.
About Us
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Headquartered in Los Angeles, California, GR0 is an award\-winning digital marketing agency focused exclusively on igniting organic growth for direct\-to\-consumer and business\-to\-business startups and established brands. GR0 empowers these brands to build powerful online brands using a highly technical, best\-in\-class approach that delivers value and experience to consumers and engagement and sales for brands.
GR0’s notable awards and achievements include:
- Glassdoor’s “Top CEOs” (2021\)
- Clutch’s “Top 100 Fastest Growing Companies” (2022\)
- Comparably’s “Best Places to Work in Los Angeles” (2021, 2022\)
- Comparably’s “Best Company for Career Growth” (2021\)
- Built In’s Moxie Women in Tech Awards (2021, 2022\)
- Great Place to Work (2021, 2022, 2023, 2024\)
- DotComm Awards (2023, 2024\)
- Power Partner's (2024\)
- Okendo Partner Award for Top Marketing Agency, North America (2026\)
At GR0, we put our people above all else. We promote an environment that celebrates diversity, fosters openness, and encourages conversations around what makes us different to further learn from one another. GR0 is proud to be an Equal Opportunity Employer. We do not discriminate based upon race, religion, color, national origin, sexual orientation, gender, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At GR0, 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
GR0 AI Hiring
GR0 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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