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About This Role
About Smartcat
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Smartcat is building the future of work, where human expertise meets digital teammates to drive 10x to 1000x productivity gains for the world’s leading enterprises.
We’re on the frontier of an entirely new category: Agentic AI. We enable enterprises to build high\-performing hybrid workforces made up of both humans and AI agents. These AI agents aren’t generic copilots. They’re fully trained digital teammates that learn from your best people, your content, and your business strategy—ready to get to work from day one.
Our platform combines generative AI, human\-in\-the\-loop workflows, and a living Enterprise Skill Graph that continuously learns and improves. Whether you're launching a product globally, onboarding new hires, translating learning content, or aligning legal teams across regions, Smartcat turns knowledge into action and action into scale.
Over 1,000 companies, including 20% of the Fortune 500, rely on Smartcat to bring their business to the world—instantly, accurately, and in every language. As a Series C company with consistently high year\-over\-year growth, we’re scaling fast and investing in people who want to shape the future of work with us.
Join us in unlocking global potential, one human and agent team at a time.
Why this role matters
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The Smartcat website is one of our most important growth surfaces. It is where prospects discover our offering, understand our value, compare us to alternatives, and decide whether to start a trial, book a demo, or engage with Sales.
As Smartcat moves beyond translation into a broader AI\-native enterprise platform, our website needs to explain a new category, rank for high\-intent demand, appear in AI\-generated answers, and convert visitors into qualified pipeline.
You will be responsible for making that happen and your mission is to turn Smartcat’s marketing website into a high\-performing growth engine by designing, building, and deploying AI workflows and automations that solve core growth problems.
You will own the website experience from discovery to conversion: improving conversion rates, optimizing landing pages, strengthening SEO and AI search visibility, and building AI\-first workflows that help the marketing team learn, ship, and scale faster.
The right person is both strategic and hands\-on: someone who can diagnose funnel friction, write a sharp landing page brief, analyze conversion data, improve technical SEO, build an AI\-assisted workflow, and ship changes without waiting for a perfect process.
What you’ll own
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### 1\. Website conversion and funnel performance
You will own conversion optimization across the marketing website, including homepage, product pages, solution pages, industry pages, pricing paths, demo pages, trial entry points, forms, CTAs, and landing pages.
### 2\. Landing pages and campaign experiences
You will build the landing\-page engine for Smartcat’s priority campaigns, personas, use cases, verticals, competitors, and product narratives.
### 3\. SEO, GEO and AI search visibility
You will help Smartcat win not only in traditional search, but also in answer engines and AI discovery surfaces by producing high\-quality, SEO/GEO\-optimized content to drive organic demand.
### 4\. AI Automation, Tools, and Workflow Systems
You will act as a *Growth Automation Designer*, defining and building AI\-powered tools and automations to solve critical problems, streamlining the marketing team's learning, shipping, and scaling processes for website growth.
### 5\. Analytics, reporting, and experimentation discipline
You will bring structure to how Smartcat measures and improves website performance.
This is not
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- This is not a general campaign\-management role.
- This is not a web project manager role where you only coordinate tickets.
- This is not a GTM Engineer role focused primarily on CRM workflows, outbound systems, or lead routing.
This is a hands\-on growth role focused on making the Smartcat website a measurable, AI\-native revenue engine. You will operate with high ownership in an evolving problem space, setting direction for how Smartcat gets discovered, understood, and converted as buyer behavior shifts across search, AI answers, and digital journeys.
Outcomes
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In this role, you will:
- Increase conversion rate across the marketing website and landing\-page funnel.
- Establish a structured experimentation program across key website surfaces.
- Build a scalable landing\-page system for campaigns, use cases, industries, personas, and product narratives.
- Improve Smartcat’s visibility across traditional search and AI search surfaces.
- Use AI to scale a high\-performing organic content program that improves search rankings and drives qualified traffic.
Design, build, and deploy high\-impact AI tools and automated systems that drastically reduce the time from insight prototype launch test* report.
- Create dashboards and operating rhythms that give the team clear visibility into website performance.
- Partner cross\-functionally to make website growth a shared revenue motion across Data, Marketing, Sales, RevOps, and Product.
- Help Smartcat communicate a new category clearly, credibly, and convertibly.
What you’ve accomplished
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You likely have:
- 5\+ years of experience in growth marketing, product management, or product\-led growth, ideally in B2B SaaS.
- Demonstrated ability to build working prototypes using AI development tools (Claude Code, Cursor, v0, or equivalent).
- Demonstrated ability and design taste to deliver beautiful user experiences, and being comfortable to do it yourself using AI design tools like Claude Design.
- Experience implementing SEO/GEO tactics that improved brand visibility across platforms such as ChatGPT, Google AI Overviews, Claude, and other answer engines.
- Proven track record in writing and managing high\-performing SEO/GEO content. You know how to align content strategy to drive measurable organic growth.
- A track record of improving website or product conversion rates. You’re maniacal about your metrics.
- Strong experimentation mindset with experience designing, executing, and analyzing growth experiments.
- Experience collaborating across highly cross\-functional environments with Marketing, Product, and Data teams.
- A bias toward shipping, measuring, learning, and improving.
What makes someone successful at Smartcat
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You will thrive here if you:
- Use AI as a force multiplier, not a novelty.
- Move fast and make progress before everything is perfectly defined.
- Focus on outcomes, not activity.
- Are analytical, but not paralyzed by analysis.
- Can turn ambiguity into a structured plan.
- Write clearly and communicate directly.
- Care about customers, not just clicks.
- Are comfortable challenging assumptions with data.
- Build systems that make the whole team faster.
- Take ownership from problem diagnosis to shipped improvement.
Smartcat’s culture emphasizes results, speed, feedback, customer devotion, and ownership; the culture code explicitly says Smartcat focuses on outcomes over process, acts fast without unnecessary bureaucracy, welcomes actionable feedback, and takes customer needs seriously.
*We are building an AI\-native product organization. If the way we described this role sounds like work you have already been doing on your own, we want to talk.*
Why joining Smartcat might be your best move so far
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- A global, connected team
We’re a team of 200\+ people across 30\+ countries, working together through a hub\-and\-spoke model anchored in eight key locations: New York, London, Lisbon, Costa Rica, Serbia, Armenia, Georgia, and Spain. Most of our roles are remote\-friendly, giving you the flexibility to do your best work from where you thrive. For customer\-facing teams, we prioritize proximity to our customers, ensuring we stay deeply connected to their needs and deliver an exceptional experience. No matter where you’re based, you’ll be part of a highly collaborative, globally\-distributed team that values ownership, speed, and meaningful impact.
- Be part of an AI Native Organization
We are highly innovative, using AI across all areas of the organization to accelerate decision\-making and free people to focus on strategy and high\-impact work. We embrace new ideas and encourage all Smartcaters, regardless of level or department, to manage their own AI Agents. At Smartcat you’ll shape how AI transforms the workplace and play an integral role in ensuring Smartcat remains a leader in AI innovation.
- Innovating a $100 Billion industry
Smartcat is reshaping the $100B multilingual content industry with an AI\-powered platform that makes it easy for companies to create, translate, and localize global content at scale. Our platform enables enterprise teams to move away from slow, traditional outsourcing methods, and achieve fast, high\-quality results, at a fraction of the cost.
- Join the rocketship to scale\-up 10x and beyond together
We are looking for someone to become an integral part of our team and play a crucial role in the most exciting part of our journey: transitioning from a post\-Series C startup to a company exceeding $100M in ARR and $1B in valuation. Our journey isn’t for the faint of heart. We are growing at 130% YoY, thanks to our strong product\-market fit and high\-performing team, and plan to accelerate from here.
- Smartcat Culture Code: Where Diversity Meets High Performance
At Smartcat, we are committed to building a culture that highlights respect and appreciation for each individual's unique background and perspective, while maintaining a strong focus on results and engagement. We believe in welcoming everyone and fostering an inclusive environment where team members can be their authentic selves at work. Our commitment to inclusion is steadfast, and we stand firmly against discrimination and harassment.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Smartcat, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Smartcat AI Hiring
Smartcat 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 $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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