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About This Role
StatBid is an operator-led e-commerce growth agency. We pair hands-on services with our own software to help Shopify merchants grow profitably.
We've run online retail businesses ourselves. We help clients find what's holding growth back and spend their marketing money well. Sometimes the problem sits outside of search, and spending more won't fix it. We'd rather earn trust over time than push for a quick sale.
We're about 30 people working across more than 14 countries. We run the company democratically, with open books. As a Certified B Corporation™, we're accountable for our impact on clients, our team, the community and the environment.
We're working toward a vision of sustainable commerce, and one of the things we find most rewarding is collaborating with people from different backgrounds and parts of the world to make a meaningful difference.
The opportunity
We're looking for an experienced e-commerce SEO practitioner to lead our organic search practice and help us build what comes next.
We've developed a 6-Pillar SEO Framework grounded in years of hands-on experience operating online retail businesses. It gives us a common approach to technical SEO, content, rankings, reporting, competitive intelligence and client-specific projects.
The framework is still early in its development. We need to better understand the quality of the work it produces, the value it creates for merchants, and where we can improve it. We're looking for someone who can help us answer those questions and turn what we learn into better ways of working.
Today, we support about 20 e-commerce sites. Our ambition is to build a system that can serve hundreds of merchants without growing our team at the same rate. That only works if we're consistently creating value for the merchants we serve.
We're also seeing consumer shopping behavior change as AI becomes part of how people discover, evaluate and buy products. Much of what makes good SEO still matters, but there are new techniques to develop, test and improve. We want someone who's passionate about e-commerce and excited to help us figure out what organic search and agentic commerce will look like in the years ahead.
You'll be our most senior SEO practitioner and the primary SEO expert our clients work with. You'll guide the team, solve difficult problems, help improve our software and take responsibility for the quality and effectiveness of our organic search work.
You'll have a team and a framework to build on, but plenty of freedom to question how things are done.
What you'll do
Lead our organic search work. Work with our e-commerce strategists and SEO team to develop client roadmaps, set priorities and make sure the work produces meaningful results. You'll be the person we turn to when SEO decisions require experienced technical judgment.
Solve complex e-commerce SEO problems. Get hands-on with technical audits, site architecture, crawling and indexing, structured data, redirects, platform migrations, product catalogs and other challenges that come with running large e-commerce sites. You won't do all the work yourself, but you'll need the skills to step in when necessary.
Work directly with clients. Be the primary SEO expert our clients interact with through monthly Zoom meetings, email, Slack and other conversations. Help them understand what we're doing, why it matters, what the results tell us and what we recommend next.
Help win new business. Participate in sales conversations, conduct SEO audits for prospective clients, identify opportunities and explain how StatBid can help. Your technical expertise and ability to connect recommendations to business outcomes will be important to earning clients' trust.
Improve the framework. Help us establish and measure first-time quality, understand where the work creates merchant value and identify where our approach falls short. Use what we learn to improve the framework, our standards and how we deliver SEO. Nothing about the framework is off-limits to questioning.
Help build better tools. Our proprietary software, Argos, is becoming an increasingly important part of how our team and AI agents identify, prioritize and execute work. You'll be an important source of feedback, helping define new functionality, improve recommendations and translate SEO expertise into repeatable processes and agent skills. You'll work closely with our engineering and data teams to make that happen.
Explore organic and agentic commerce. Keep learning as search and shopping behavior evolve. Develop hypotheses, test new approaches and help us understand what actually improves merchant visibility and results across traditional search and emerging AI platforms.
Help others do better work. Coach team members, review the quality of their work, share your expertise and help improve our standards. Our SEO support team has its own manager, so this isn't primarily a people management role. Your influence will come through your expertise, collaboration and willingness to help others grow.
Keep clients on track. Make sure client performance is monitored, problems are identified early, recommendations are acted on and we follow through on commitments. Protecting the quality of our existing work and client relationships comes first.
Who we're looking for
We're looking for someone who knows e-commerce SEO deeply, enjoys working with people and is excited about building better ways to get results.
We're more interested in what you can do, how you think and how you learn than how many years you've been doing it.
Here's what matters most to us:
- Strong e-commerce SEO expertise. You understand technical SEO, content strategy, site architecture, crawling and indexing, structured data, redirects, migrations and the other challenges involved in growing organic traffic and revenue for online retailers. You can both diagnose problems and help implement solutions.
- Experience with Shopify. Shopify is central to our work, so strong experience with the platform is highly desirable. We'd consider someone exceptional from another e-commerce platform who can demonstrate an ability to get up to speed quickly.
- An understanding of how online retail works. You recognize that rankings and traffic aren't the ultimate goal. You can connect SEO decisions to merchandising, inventory, conversion, revenue and profitability. Experience working inside an e-commerce business is especially valuable, but not required.
- Good judgment and ownership. You can distinguish important problems from distractions, set priorities and take responsibility for outcomes. When something isn't working, you find out why and do something about it.
- A willingness to build systems. You enjoy figuring out how to make good work repeatable. You look for opportunities to improve processes, eliminate unnecessary work and use technology to make the team more effective. You don't need to have built a system like ours before.
- Curiosity and adaptability. You're passionate about e-commerce, organic search and emerging agentic commerce channels. You actively learn new technologies, test ideas and change your approach when the evidence suggests something better.
- Clear communication. You can explain complicated technical subjects in language clients understand, make recommendations confidently and have difficult conversations when needed.
- A collaborative approach. You enjoy working with others, sharing what you know and helping people develop. Previous coaching experience is a plus, but genuinely caring about others and their growth matters more.
Our values, the Seven Cs, shape how we work together: Candid, Conscientious, Collaborative, Constructive, Creative, Curious and Non-Conformist.
We expect people to speak up when something is wrong, be honest about what they don't know, question existing practices and help others do good work.
Tools we use
Our tools include Shopify, Google Search Console, GA4, SEMrush, Screaming Frog, Monday.com, Confluence, Claude and our own proprietary software, Argos.
We're increasingly using AI agents to support our work. You don't need to be a software engineer, but you should be comfortable learning new tools, helping develop agent skills and working with our technical teams to improve what those systems can do.
The details
This is an ongoing, remote role at 35+ hours per week, with paid time off.
You'll work with a distributed international team and will need to maintain North American working hours to collaborate with our clients and team.
Pay: $35.00 - $50.00 per hour
Expected hours: No less than 35.0 per week
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Application Question(s):
- Why this role specifically, and how does it fit where you're trying to take your career over the next few years?
- Tell us about a professional project you're proud of: what it was, why you're proud of it, and what you'd do differently if you did it again.
- StatBid runs on seven core values, our 7 Cs. Read through them, then tell us which one resonates most with you and which you think would be hardest for you to live up to. Give a specific example from your own work or life for at least one.
- How do you de-stress? Is it a routine thing, or more of an escape hatch you reach for when things get bad?
Work Location: Remote
Salary Context
This $72K-$104K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At StatBid, 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($88K) sits 58% below the category median. Disclosed range: $72K to $104K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
StatBid AI Hiring
StatBid has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $104K - $104K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,431 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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