Interested in this AI/ML Engineer role at lynton?
Apply Now →Skills & Technologies
About This Role
About Lynton
----------------
Lynton spent 16 years as a HubSpot partner: 2,000\+ projects across 50\+ industries. We know SaaS and agency work from the inside. When we saw what's coming with AI, we left HubSpot to build what comes next.
We build and run AI\-native growth systems for mid\-market companies: measurement\-first websites, the sovereign stack that replaces legacy software, and AI agents we deploy to run the marketing, sales, and operations that used to live in hand\-built workflows. The client owns the infrastructure, we operate the agents, and the whole system proves what it produces in revenue. We're AI\-native in how we operate, and we're building products, solutions, and a whole new kind of company from the ground up.
Click here to apply directly on our website
We prioritize candidates who apply on our site. You do not need to submit a second application here.
The opportunity
-------------------
You excel and thrive with this kind of work. You've driven measured results for clients: pipeline, bookings, revenue, not just deliverables. You've led the work, and probably the people. And you're done watching your agency treat AI as a pilot project while it rewrites how the whole business gets done.
Here, AI is the operating model. As our founding strategy leader, you own the strategy layer of every engagement: the assessment and roadmap phase that opens each project, the measurement framework that proves what the work produced, and the marketing\-and\-sales strategy that grows a launch into an ongoing engagement. You expand client accounts, including the AI agents we deploy to run their marketing, sales, and operations. You direct a fleet of AI agents through the production research and reporting, so your hours go to judgment and client leadership instead of throughput. You build the playbook and the frameworks from a blank page, and the team behind them as we grow.
A lot of that work is helping clients leave tools they have outgrown. You scope and lead migrations off legacy platforms (CRM, Marketing Automation, website CMS), and design the autonomous agents and AI automations that replace the workflows people used to hand\-build. If you have spent years implementing and migrating client systems, this is where that experience compounds instead of repeating.
What you'll own
-------------------
- Strategy and blueprint phases. Lead the phase that opens every engagement: digital estate assessments, technology\-stack assessments, stakeholder sessions, and phased roadmaps with budget allocation. You produce decision\-ready packages leadership approves with confidence.
- Systems adoption and migration. Lead clients off tools they have outgrown and onto AI\-native infrastructure: scope the migration, design the autonomous agents and AI automations that replace brittle workflows, and own the cutover plan. You set the architecture and direct the build team and agents on execution.
- Measurement frameworks that prove revenue. Run measurement\-definition workshops with client teams: what counts as a lead, how revenue attributes across marketing and sales, which offers run web\-only so redemption proves attribution. Establish baselines, define the dashboards, own the monthly story.
- Marketing and sales strategy. Funnels, conversion, promotions and campaign systems, lifecycle and abandoned\-intent recovery, and the handoffs where marketing becomes pipeline and pipeline becomes revenue. You define what the systems need to do; the build team makes them do it.
- Search and AEO. Own organic and local SEO across multi\-location businesses and answer\-engine optimization. Our own migration grew AI\-referred traffic about twelvefold, and you'll build the practice that produced it into a repeatable capability for clients.
- Paid\-media readiness and advisory. Own the tracking foundation and advise clients shifting budget to digital: ROAS targets, channel mix, what to spend where once the numbers are trustworthy. This is the expansion lane, where you grow launched projects into ongoing engagements.
- Client leadership. Present to VPs and executives and earn the decision. Translate one plan into per\-stakeholder outcomes across marketing, sales, search, and the executive team. Surface the next phase at the moment the data justifies it.
- AI\-first delivery. Direct our agent fleet on strategy work: audits, competitive research, content and funnel analysis, reporting. Manage the per\-engagement AI inference budget like any other resource. Build reusable agent workflows so every engagement makes the next one faster.
- Agents you deploy for clients. Beyond the internal fleet, you scope, build, and operate AI agents on the client's behalf: running their ads and campaigns, qualifying and routing leads, handling lifecycle and support, keeping their reporting current. These are recurring engagements you own and grow, not one\-time projects.
Who you are
---------------
- 8\+ years driving measurable results for clients in digital marketing and sales strategy. The number you point to is revenue, pipeline, or bookings you moved, not deliverables you shipped.
- Agency\-grown. You've built your career inside agencies. You know the model from the inside (the pod, the billable hour, the retainer treadmill) and you can see where AI breaks it open.
- You have worked inside clients' stacks. You have implemented, integrated, and migrated marketing and CRM systems for clients, so you know what breaks in a migration and how to de\-risk a cutover. HubSpot ecosystem depth is a strong plus given our client base and history.
- You've led. You've run a team, a client pod, or major engagements end to end and answered for the outcome. Or you're the senior everyone already treats as the lead, and you're ready for the title. Experience or clear potential both count.
- Deep analytics fluency: GA4, GTM, conversion tracking, attribution modeling, dashboard design. You've built measurement frameworks, not just read the reports.
- Local SEO depth, plus real curiosity about AEO and AI search. You can explain llms.txt to a VP and defend why it matters.
- Frustrated in the right way. You're doing well where you are and could stay. What's pulling you is watching your agency treat AI as an experiment while you can see it changing the whole business.
- Ready to build, not just manage. If you run a team today, you're ready to trade it for a blank page and an agent fleet, and build a function (eventually a cross\-functional agency team) from the ground floor.
- AI\-first in practice. You already work with AI agents or LLM\-driven workflows day to day. You can spec a task, judge the output, and improve the loop. Directing agents through the production work is the appeal, not the threat.
### Bonus
- Hands\-on paid\-media management
- Booking or e\-commerce funnel optimization
- RevOps or CRM/martech implementation and migration experience
- Replacing legacy workflow automation with code or AI agents
- Familiarity with modern open\-source web stacks
PEwAQKf1w9
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At lynton, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
lynton AI Hiring
lynton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.