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About This Role
About Lynton
Lynton spent 16 years as a HubSpot partner, delivering 2,000+ projects across 50+ industries. We know SaaS and agency work from the inside. When we saw what was coming with AI, we left HubSpot to build what comes next.
We build and run AI-native growth systems for mid-market companies: websites built to be measured, an open-source stack that replaces legacy software, and AI agents that run marketing, sales, and operations. The client owns the infrastructure, we operate the agents, and the whole system has to show what it produces in revenue.
If this role sounds like a perfect fit for you, skip ahead to apply directly on our website. We prioritize candidates who apply directly on our site.The opportunity
Most agencies have added AI to their production line and kept the creative process they had ten years ago. We started over. AI coding agents build our websites. Our imagery comes from image and video models we direct. The interactive work that used to need a studio and a six-figure budget is now something a small team can ship in weeks.
What that makes possible depends on taste. Anyone can generate a thousand images. Very few people can pick the right one, fix what's wrong with it, and make it move in a way that serves the page's argument. That's the job.
As our founding creative director, you own the creative and communication strategy for every website and campaign Lynton ships, both ours and our clients'. You decide what a page says and how it looks. You design how it moves. You make or direct every image, illustration, animation, and film on it. Some weeks you're writing the headline. Other weeks you're lighting a scene in Blender, or telling a client their brand deserves better than stock photography.
The agency is one part of what we're building. Lynton is also a launchpad for new products, services, and companies we incubate from inside, and with your help we'll take each of them to market.
What you'll own
- Creative and communication strategy. Own the messaging and visual system for every site and campaign. That covers positioning, page structure, headlines, body copy, the design language, and the design tokens that encode it. You write copy yourself and edit everything that ships, so the words and pictures come from one point of view.
- Interactive experience design. Design how every page moves and responds, including SVG and CSS animation, scroll-driven storytelling, micro-interactions, and real-time 3D. You storyboard it, prototype it, and direct the build. When getting into the timeline yourself is faster than explaining, you do that.
- 3D and real-time scenes. Model, light, and art-direct scenes in Blender, then bring them to the browser with Three.js and WebGL. You set the bar for when a page earns real-time 3D and when a lighter technique tells the story better.
- Generated photography and illustration. Build and run the image pipeline. You'll art-direct photography and illustration with image models, keep one style consistent across a whole site or campaign, and retouch what the model gets wrong. You'll also animate stills with video models. You set the house rules on style, consistency, and licensing.
- Video and motion graphics. Produce product films, launch and explainer videos, social cuts, and motion graphics. Use generative video, compositing, and traditional motion tools in whatever mix gets the best result.
- Campaign creative. Own launches and email campaigns end to end: concept, copy, landing pages, imagery, and video. A campaign should read as one idea from the inbox to the page.
- New products, services, and brands. Lead the creative for every launch we incubate: the name and identity, the positioning and story, and the site and campaign it goes to market with.
- Creative oversight on every engagement. You review and sign off on all creative the agency ships. You're the reason a Lynton site looks and reads like one, and the reason no client site looks like a template.
- Performance and accessibility as part of the craft. We hold every page to a mobile Lighthouse score of 90 or above and to WCAG AA contrast. Motion respects reduced-motion settings. 3D loads after the headline, so it never delays the first thing a visitor reads. Copy lives in the page, where both people and AI assistants can read it.
- AI-first production. Direct our agent fleet through the production work: coding agents for prototypes and builds, image and video models for assets, and language models for first drafts and variants. Manage each engagement's inference and render budget like any other resource. Build reusable pipelines so every project starts further ahead than the last.
Who you are
- 8+ years in digital creative, with at least three leading it. You've been a creative director, associate CD, or design lead at an interactive studio, an agency, or an in-house brand team. You've answered for the work across many projects at once.
- A portfolio of interactive work you directed and helped build. Your sites use motion to carry the story rather than to decorate it. Awards are nice. Work we can click through and feel is what counts.
- You write. You can turn a positioning brief into a homepage headline and the three paragraphs under it. You can edit someone else's copy until it sounds like a person. To you, words and pictures are one job.
- Hands-on across the motion stack. You're fluent in CSS and SVG animation, GSAP or an equivalent, and scroll-driven sequencing. You've shipped real-time 3D in the browser with Three.js, React Three Fiber, or similar, and built or lit scenes in Blender, Cinema 4D, or Houdini. You don't need to be the strongest engineer in the room, but you can read the code, prototype with a coding agent, and tell when a build is wrong.
- Deep, current practice with image and video models. You use them every day and have opinions about which to use for what. You know how to keep a character, a product, or an illustration style consistent across dozens of outputs. You also know where the models still fail.
- Motion and video craft. You can cut a film, design motion graphics, and finish in After Effects, Premiere, DaVinci Resolve, or their equivalents, alongside generative tools.
- Taste you can explain. You can say why a typeface, a cut, or a camera move is right, in terms a client or an engineer can act on.
- Honest with the medium. You'd never present a generated person as a real customer. You know the commercial terms of the models you use. You'd rather ship an honest illustration than a misleading photograph.
- Performance-minded. You've traded a heavier scene for a faster page and can explain the call. Texture budgets, draw calls, and Core Web Vitals are part of your craft vocabulary.
- AI-first in practice. You already direct agents across design, code, and copy. You can spec a task, judge the output, and improve the loop. For you, directing agents is the appeal rather than the threat.
- Ready to build. You're comfortable with a blank page, a small team, and a leadership group that builds alongside you.
Bonus
- Shaders, GLSL, or generative art
- Rive, Lottie, or Spline for lightweight interactive motion
- Sound design for web and film
- Brand identity systems built from scratch
- Training or fine-tuning image models on a brand's style
- B2B or professional-services clients
Why this role
- Ground floor with credibility. We have 27 years in business, 16 years of enterprise relationships, and a client base that generates revenue. You'll build the creative function of a new kind of agency on a real foundation.
- The whole creative surface. Strategy, words, design, interaction, 3D, image, and video all sit with one person. There's no handoff between a copy team, a design team, and a motion vendor.
- The new tools are the daily workflow here. The models and agents most studios are still piloting are a budget line and an everyday habit at Lynton.
- Direct to leadership. You work directly with the CMO and the founder, with no creative committee in between, and your work ships to real clients.
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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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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 $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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