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About This Role
About Airwallex
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Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 200,000 businesses worldwide – including Brex, Rippling, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 2,000 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 billion and backed by world\-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.
Attributes We Value
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We hire successful builders with founder\-like energy who want real impact, accelerated learning, and true ownership. You bring strong role\-related expertise and sharp thinking, and you’re motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.
You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end\-to\-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.
About the team
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The Marketing team at Airwallex drives brand awareness and customer engagement through innovative and strategic campaigns. We work to communicate the value of our financial solutions, attract new customers, and strengthen relationships with existing ones. By leveraging data\-driven insights and creative strategies, we ensure Airwallex stands out in a competitive market. Our team is passionate about telling the Airwallex story and supporting the company's growth and success.
What you’ll do
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We’re looking for a data\-driven, strategic, and hands\-on Paid Social Manager to lead acquisition and full\-funnel demand generation campaigns across LinkedIn and Meta. This role will focus on enriching, segmenting, and scaling audiences to reach SME and mid\-market decision\-makers globally — ensuring every dollar spent drives measurable impact on pipeline and revenue.
You’ll blend analytical rigor with creativity, partnering with growth, product marketing, and analytics teams to design audience frameworks, run experiments, and develop platform\-native campaigns that convert. The ideal candidate thrives in fast\-paced environments and understands how to connect audience data, ad systems, and CRM workflows into a cohesive, high\-performing growth engine.
This role is based in San Francisco.
Responsibilities:
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- Own end\-to\-end LinkedIn and Meta campaign strategy, build, and optimization across the funnel (awareness, consideration, conversion).
- Design and scale audience enrichment frameworks to effectively target SMEs and mid\-market segments — leveraging firmographic, behavioral, and CRM\-based signals.
- Collaborate with Product Marketing to map content and creative to each buying stage and audience cohort.
- Manage pacing, bid strategies, and budget allocation to deliver performance within CAC and ROI targets.
Maintain campaign QA, naming conventions, and tracking integrity (UTMs, pixels, offline conversions).
- Build and maintain audience architectures using first\-party CRM data, lookalikes, and retargeting pools.
- Partner with RevOps and Analytics to develop audience segments sourced from Salesforce and Marketo, ensuring alignment with sales territories and lifecycle stages.
- Use SFDC reports and Marketo Smart Lists to enrich or suppress audiences, ensuring campaign relevance and lead quality.
- Query campaign and CRM data using SQL or data tools (e.g., BigQuery, Snowflake, Looker) to identify patterns and optimization opportunities.
Test new audience and data onboarding approaches (e.g., Clearbit, 6sense, ZoomInfo, or LinkedIn Matched Audiences).
- Build a structured testing roadmap across creative, bidding, and audience strategies.
- Analyze funnel metrics (CTR, CVR, MQL\-to\-SQL, pipeline contribution) and surface actionable insights.
- Run incrementality, brand lift, or geo\-lift studies to measure true business impact.
- Partner with analytics to refine attribution visibility and improve full\-funnel ROI reporting.
- Collaborate with Performance Strategy, Creative, and Regional Marketing to localize global frameworks.
- Partner with Sales and RevOps to ensure alignment on lead routing, scoring, and follow\-up SLAs.
- Work with Finance and Data teams to reconcile spend, pacing, and forecast models.
- Serve as the internal SME for LinkedIn and Meta best practices, audience enrichment, and data integration.
- Platforms: LinkedIn Campaign Manager, Meta Ads Manager, Google Tag Manager, and native ad tools for creative and tracking management.
- Data \& Querying: SQL, BigQuery, or Looker for analyzing campaign and audience data.
- CRM \& Automation: Salesforce (SFDC) and Marketo — including Smart Lists, workflows, and lead lifecycle integration.
- Attribution \& Tracking: GA4, UTMs, pixel tracking, and offline conversion imports.
- Analytics \& Reporting: Experience building Looker/Tableau dashboards or structured performance reports.
- Audience Tools: Familiarity with enrichment and ABM tools such as 6sense, Clearbit, or ZoomInfo is highly preferred.
Who you are
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We're looking for people who meet the minimum requirements for this role. The preferred qualifications are great to have, but are not mandatory.
Minimum qualifications:
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- 5\+ years of experience in B2B paid social marketing, with proven success on LinkedIn and Meta.
- Demonstrated ability to scale campaigns for SME and mid\-market audiences with measurable pipeline impact.
- Hands\-on experience with Salesforce and Marketo, connecting campaign data to leads, contacts, and opportunities.
- Strong analytical mindset — capable of querying, segmenting, and transforming audience data independently.
- Deep understanding of paid media metrics, attribution, and ROI modeling.
- Excellent project management and communication skills; ability to collaborate across marketing, sales, and analytics.
- Bachelor’s degree in Marketing, Business, Data Analytics, or related field.
Preferred qualifications:
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- Experience in fintech or SaaS environments targeting global or multi\-segment audiences.
- Familiarity with multi\-touch attribution, MMMs, and data privacy frameworks.
- Comfort working across multiple time zones and global marketing teams.
- Experience guiding creative optimization and A/B testing processes.
- Bilingual proficiency in Cantonese or Mandarin to support regional campaign localization and stakeholder collaboration.
- Multi\-lingual and particularly Korean, Japanese or French.
Applicant Safety Policy: Fraud and Third\-Party Recruiters
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*To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @**airwallex.com* *email address. Please apply only through* *careers.airwallex.com* *or our official LinkedIn page.*
*Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.*
Equal opportunity
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Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.
Compensation Range: $110K \- $170K
Salary Context
This $110K-$170K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Airwallex, 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. This role's midpoint ($140K) sits 16% below the category median. Disclosed range: $110K to $170K.
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.
Airwallex AI Hiring
Airwallex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $170K - $170K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% 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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