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About This Role
Context \& Impact
As part of Lansweeper’s global marketing engine, the Paid Media Specialist plays a pivotal role in shaping how we reach, influence, and convert our target audiences across digital channels. You will translate Lansweeper’s strategic goals into integrated, data‑driven paid media initiatives that strengthen brand visibility, accelerate demand, and enable predictable, scalable pipeline creation.
Your Goal
Own and lead Lansweeper’s paid media strategy across global market. You will design full\-funnel paid media programs, define a long‑term vision for digital advertising, driving measurable performance improvements that support both brand and revenue objectives.
Challenge
The main challenges you will face are:
- Building a cohesive global paid media strategy that spans multiple business motions while ensuring alignment with broader marketing goals.
- Managing complex budgets and channel mix across SEA, display, programmatic, paid social, paid video, ABM, Referrals (Software Review Platforms), etc.
- Ensuring strong performance measurement, attribution clarity, and data‑driven decision‑making in a rapidly evolving digital landscape.
- Leading cross‑functional collaboration to embed paid media best practices into integrated campaigns and product launches.
- Working closely with the product\-led growth (PLG) engine to align on strategy and ensure clear ownership. *PLG will lead the strategy for Free / Trial conversions, while Paid Media focuses on generating a strong lead funnel and pipeline from non‑trialist CTAs, requiring continuous alignment, shared insights, and synchronized activation across the marketing ecosystem.*
Key Responsibilities
- Develop and own Lansweeper’s global paid media strategy across search, display, programmatic, paid social, paid video, ABM, Referrals (Software Review Platforms), and other channels.
- Translate business goals into pipeline‑generating paid programs, ensuring alignment with the overarching marketing strategy.
- Define a comprehensive experimentation roadmap, including budgeting for testing, channel prioritization, and A/B hypotheses.
- Oversee global budget forecasting, allocation, and pacing across all paid media channels.
- Guide conception‑to‑launch planning of digital campaigns, ensuring optimal channel mix, targeting, and messaging.
- Establish and refine performance measurement frameworks, including KPI governance, dashboards, attribution models, and reporting cadence.
- Provide strategic insights and recommendations to marketing leadership on performance trends, investment strategy, and pipeline impact.
- Partner closely with content, product marketing, campaign owners and PLG to create high‑performing messaging and creative assets.
- Stay ahead of industry trends, platform evolutions, and new technologies to maintain Lansweeper’s competitive edge in digital advertising.
- Manage relationships with agencies, platform partners, and external vendors.
Key Requirements
Hard Skills
- 4\+ years in paid media, performance marketing, or digital strategy with proven success in scaling campaigns across B2B environments; SaaS or agency experience preferred.
- Bachelor’s degree in Marketing, Digital Marketing, Communications, Business, or a related field.
- Expert knowledge of SEA paid social, programmatic buying, retargeting, and multi‑channel campaign orchestration.
- Strong analytical capability with experience in KPI definition, performance modelling, reporting, and attribution frameworks.
- Proficiency with analytics suite (Google Analytics, Google Tag Manager, Looker Studio) and advertising platforms such as Google Ads, LinkedIn Ads, Meta, and programmatic DSPs.
- Experience overseeing budgets and optimizing ROI, CPL, and pipeline contribution.
- Familiarity with CRM and marketing automation (Salesforce, Pardot)
- Relevant certifications that strengthen candidacy, such as: Google Ads Certification, Google Ads Professional Certification, Google Analytics Certification, Microsoft Advertising Certification, Meta Blueprint Certification, LinkedIn Marketing Solutions Certification, Pardot / HubSpot Digital Marketing or Inbound Certification, any recognized programmatic training (e.g., DV360\)
Going for gold: (plus point but not mandatory)
- Experience with ABM platforms (6Sense, N.Rich)
- Experience with DV360, Search360
- Experience with G2 marketing capabilities
- Experience with Google Cookie Consent Mode
Soft Skills
- Strategic thinker with the ability to translate insights into high‑impact actions. AI paid trends
- Excellent communication and stakeholder‑management skills, able to influence cross‑functional teams and senior leadership
- Strong organizational skills with the ability to manage multiple complex projects in a dynamic environment.
- Data‑driven, curious, and proactive, continually seeking opportunities for improvement
- High accountability, problem‑solving mindset, and willingness to challenge the status quo.
Our Offer
- Competitive salary aligned with market benchmarks
- Benefits package adapted to local regulations
- Career growth and continuous learning opportunities
- Hybrid work model for employees near a Lansweeper office (minimum 2 days per week onsite).
- Collaborative, global team environment across Europe and North America
Company Info
Lansweeper is the leader in Technology Asset Intelligence, providing a single source of truth for complete visibility across IT, OT, and IoT assets — including software — across the entire technology estate. Since 2004, Lansweeper has empowered organizations to stay competitive with accurate, actionable data that strengthens security, drives strategic decisions, and boosts operational efficiency.
Trusted by over 20,000 customers — including CMA CGM, Rentokil, Fiskars, Nestlé, and Nvidia — Lansweeper serves enterprises, governments, financial institutions, NGOs, and universities worldwide.
Team Info
You will be part of our Center of Excellence Marketing Team reporting into our Global Growth Marketing Director.
Call to Action
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For US‑only Applicants: Diversity Statement – Equal Employment Opportunity
It is Lansweeper’s policy to provide equal employment opportunity to all applicants and employees. Lansweeper disapproves of, and will not tolerate, unlawful discrimination against any applicant or employee because of race, color, national origin or ancestry, gender (including pregnancy, childbirth, or related medical conditions), gender identity, age, religion, disability, family care status, veteran status, marital status, sexual orientation, or any other basis protected by local, state, or federal laws
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Lansweeper, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Lansweeper AI Hiring
Lansweeper has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national median.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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