Interested in this AI/ML Engineer role at Marketech Digital Solutions?
Apply Now →Skills & Technologies
About This Role
Job description:
Remote (US hours required) \| Full\-time or Contract
Marketech is a boutique email marketing agency serving mid\-market DTC brands ($10–100M in revenue) on Shopify and Klaviyo. \~15 people across strategy, copy, design, and operations.
We're investing heavily in the Claude Code ecosystem to power vibe marketing — AI agents handle most of the execution; humans direct context, strategy, and quality. Our monorepo of skills, agents, and workflows is the canonical operational system behind that bet. The repo is new, the investment is significant, and *the architectural decisions made over the next 12 months will shape how we operate for years.*
*This role owns the engineering and architecture of that system.*
The Role
You'll lead engineering for our internal tools and AI infrastructure. You own how the system gets built: when to use n8n versus when to deploy our own service, how to design Python pipelines that scale past spreadsheets into a real data lake, how to choose between managed services and self\-hosted infrastructure, and how the agent monorepo evolves architecturally as we grow from \~50 skills today into the operating system for the whole company.
Product direction — the "what to build" — is owned by the company's leadership and operations team. They know what we're trying to accomplish and what work needs to flow through the system. Your job is the "how": turning their direction into well\-scoped engineering work, executing on the architecture, and making sure today's solutions don't become tomorrow's tech debt.
You're senior enough to push back when a stated request points at the wrong technical solution, and to design a better one. You're hands\-on enough to build alongside your team every day.
The four things that matter most
*1\.* *Architectural judgment.* *When we hit a real problem — "we need this Google Sheet to update across 10 client accounts" — you decide whether the answer is a Make scenario, an n8n workflow, a Python script in our monorepo, a managed service, or a self\-hosted database. You defend the decision on TCO, scalability, observability, and team capability, not just technical elegance.*
*2\.* *System\-level engineering.* *You design for the system, not the request. Single\-purpose scripts that solve one ticket but don't compose with the rest of the stack are a liability. Your work creates capability for everything we build next.*
*3\.* *Build/buy/skip judgment.* *Knowing when to ship the boring buy, when to build the differentiator, and when to refuse to solve a problem that shouldn't exist in this form.*
*4\.* *Coaching engineers and contributors to ship right.* *Two direct reports and a semi\-managed contributor today. Your job is to make them faster and safer through process, code review, and architectural standards — not to do their work.*
What you'll own
- The Marketech monorepo — our canonical operational system, built on the Claude Code stack. Currently \~50 agent skills and \~13 agents driving our vibe marketing operations and data analytics. You own the engineering architecture: how skills compose, how agents call them, how data flows through, and where the system fails gracefully.
- The Chief Bot Officer — a Slack bot that answers operational questions for the team. Currently on Python/Flask; planned migration to a managed agent architecture (essentially a Slack UI on the Marketech monorepo). You own the migration design.
- The n8n orchestration layer — workflows connecting Klaviyo, Monday.com, Google Workspace, Slack, and client tooling. You decide what stays in n8n versus what graduates into the monorepo as proper code.
- The data layer — our Google Sheets, Klaviyo exports, Shopify data, and operational reports today live in disparate places. You decide what a real data foundation looks like — managed warehouse, self\-hosted, or composite — and bring it into being.
- The roadmap — what gets built next, by whom, and why. Co\-owned with company leadership; they bring the product priorities, you bring the engineering plan.
- The team — 2 direct reports and 1 semi\-managed contributor today, with room to grow. Your job is to scale them through process and coaching, not to do their work.
What you'll do day\-to\-day
- Translate ambiguous business asks ("we need to know AOV by segment across all clients") into scoped engineering work — data model, integrations, observability, error paths.
- Decide what we build, what we buy, what we self\-host, and what we skip. Defend with reasoning.
- Architect at the system level — push back when a proposed approach won't scale, won't compose, or creates tech debt you'll be paying down in six months.
- Coach your reports through scoping, code review, prioritization, and communication.
- Build alongside your team, every day. You'll ship Python services, n8n workflows, agent skills, and infrastructure changes yourself. This isn't a pure management role. Your daily output sets the engineering standard contributors learn from.
What we're looking for
- 5\+ years of software engineering experience building and operating systems in production. Internships and student work do not count toward this minimum.
- Real architectural chops. You've made build/buy/host\-it\-yourself decisions for production systems, and you can talk through the tradeoffs concretely.
- Production engineering credentials. You've deployed services, designed pipelines, instrumented observability, and operated something that mattered. "Vibe\-coded one app" is not what we're after.
- Comfortable across the stack — Python \+ JavaScript/TypeScript, APIs, databases, queues, message buses, cloud services.
- Hands\-on with the agent ecosystem — Claude Code, MCP, agent skills, workflow automation platforms (especially n8n). You're not just a consumer; you build with these.
- Experience evaluating build vs. buy through a business lens. Cost, ROI, maintainability count as much as technical elegance.
- Comfortable translating ambiguous requests into scoped engineering work — separating stated wants from underlying technical needs.
- Self\-directed and execution\-focused. This is a senior remote role with real ownership.
Bonus points
- Engineering management or technical lead experience — you've coached engineers, set code review standards, designed sprint and intake processes.
- Experience building AI\-enabled workflows or internal tools at scale.
- Background in martech, e\-commerce, agency, or DTC environments.
- Familiarity with the Shopify / Klaviyo ecosystem.
- Distributed team experience.
- Data engineering background — warehousing, pipelines, dbt\-style modeling.
None of these are required. If you think you're a strong fit for the role, apply — even if you have few or none of these. We weigh how you think and what you've built over what's on your resume.
Why this role
Internal automation and AI infrastructure is the \#1 long\-term strategic priority for our services business. It's what lets us deliver better work to clients with a leaner team — and it's the foundation we're building the next chapter of the company on.
You'd be the senior engineering owner of that initiative, reporting directly to the owner. Every person at Marketech benefits directly from your work — the long\-term goal is for everyone in the company to use Claude Code as their daily interface for the work they do.
Over time, this role can expand to engineering ownership across other Marketech initiatives, including our Restore.ai product.
Logistics
- Remote, hiring globally
- US working hours required — your choice of Eastern, Central, or Pacific. Wherever you live, you keep US hours.
Benefits:
- Flexible schedule
Application Question(s):
- What automation or AI tools are you actively building with right now? (Be specific)
- Why this role?
- This is a contract role. What monthly salary or hourly rate are you looking for?
- Describe your background in software engineering, system architecture, and automation.
- Walk us through a system architecture decision you've made. When did you choose to buy a SaaS vs. deploy your own service, or design a scalable pipeline from scratch? What did you decide and why?
Work Location: Remote
Salary Context
This $20K-$208K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Marketech Digital Solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($114K) sits 37% below the category median. Disclosed range: $20K to $208K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Marketech Digital Solutions AI Hiring
Marketech Digital Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $208K - $208K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.