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About This Role
WHO WE ARE
At Trustly, we're building a smarter, faster, and more secure financial future by revolutionizing the world of payments. As a global leader in Open Banking Payments, we are establishing Pay by Bank as the new standard at checkout, providing unparalleled freedom, speed, and ease to millions of consumers and merchants worldwide.
Our Ambition: To build the world’s most disruptive payment network and redefine what the payment experience should feel like.
Trustly is a global team of innovators, collaborators, and doers. If you are driven by a strong sense of purpose and thrive in a dynamic, entrepreneurial, and high\-growth environment, join us and be part of a team that’s transforming the way the world pays.
### ABOUT THE AI ENABLEMENT TEAM
The AI Enablement team sits at the intersection of technology, product, and people — responsible for accelerating Trustly's capacity to build and scale AI\-powered capabilities across the organization. The team partners deeply with Engineering, Product, Data, and Operations to identify high\-leverage opportunities, drive adoption of AI tooling, and establish the frameworks that let Trustly move faster and smarter.
### ABOUT THE ROLE
As VP of AI Enablement, you will define and lead Trustly's internal AI strategy — translating company priorities into a coherent roadmap for how we build, deploy, and scale AI across products and teams. Reporting to the CTO (or equivalent executive), you'll own everything from foundation model selection and agentic tooling adoption to the cultural and organizational change required to make AI a genuine competitive advantage for Trustly. This is a high\-impact leadership role for someone who thinks in systems, moves with urgency, and has done this before.
### WHAT YOU'LL DO
- Define and own Trustly's AI enablement strategy, roadmap, and success metrics — ensuring alignment with engineering, product, and business leadership.
- Build and lead a high\-performing team of AI engineers, platform engineers, and ML practitioners focused on internal tooling, developer productivity, and AI\-assisted product development.
- Drive the evaluation, adoption, and integration of foundation models, LLM orchestration frameworks, and agentic systems into Trustly's core infrastructure and product surfaces.
- Establish governance and standards for responsible AI use — including model evaluation, safety, cost management, and quality frameworks — across the organization.
- Partner with Product and Engineering leaders to identify and prioritize AI\-powered features and automations that deliver measurable business impact across payments, risk, and operations.
- Partner with leaders across every function — finance, HR, communications, legal, and operations — to bring agentic workflows and AI\-native ways of operating to their teams in a structured, ROI\-positive way, with clear prioritization and measurable productivity gains.
- Serve as a thought leader and internal evangelist for AI — running enablement programs, building internal communities of practice, and creating the conditions for teams to experiment and ship.
### WHO YOU ARE
- 10\+ years of experience in technology leadership, with at least 3 years directly leading AI/ML platform, infrastructure, or enablement teams at scale.
- Deep fluency in modern AI tooling — including LLMs, RAG architectures, agentic frameworks (e.g., LangChain, CrewAI), and model evaluation practices — with the technical credibility to engage senior engineers.
- Proven track record of driving AI adoption at a company level: building the programs, platforms, and partnerships that move AI from experimentation to production.
- Strong operator with experience building and growing teams, managing cross\-functional stakeholders, and delivering results in fast\-moving, high\-ambiguity environments.
- Fintech or payments domain experience a strong plus; comfort operating in regulated, high\-reliability environments required.
- Excellent communicator — equally fluent in executive strategy conversations and hands\-on technical reviews.
$350,000 \- $420,000 a year
Applications for this role are accepted on an ongoing basis.
SALARY RANGES IN US\-BASED ROLE POSTING
Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Recruiters can share more information with applicants about the specific salary range for preferred locations during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only and do not include other perks and benefits.
WHAT WE OFFER
At Trustly, you’ll have the chance to solve meaningful challenges alongside some of the brightest minds in FinTech. Together, we’re shaping the future of payments in an environment that celebrates curiosity, collaboration, and innovation. You'll be challenged and empowered to grow, making a real impact every step of the way.
Our team is as diverse as the global footprint we serve, with colleagues across Silicon Valley, the U.S., Canada, Brazil, Europe, and beyond. At Trustly, we foster a workplace where everyone feels they belong—a place where teamwork thrives, ideas flourish, and we never forget to have fun along the way.
We offer innovative perks and benefits packages that include:
- Flexible paid time off \& generous PTO accrual plans
- Comprehensive medical, dental, vision, and other insurances
- FSA \& HSA plans for medical and dependent care
- Home office set\-up allowance
- Internet stipend
- Retirement plan match for 401k and RRSP
- Gender\-neutral paid parental leave, and more!
*(The benefits and total compensation packages outlined above are for full\-time employees; some exclusions apply for temporary positions.)*
At Trustly, we embrace and celebrate diversity of all forms and the value it brings to our employees and customers. We are proud and committed to being an Equal Opportunity Employer and believe an open and inclusive environment enables people to do their best work. All decisions regarding hiring, advancement, and any other aspects of employment are made solely on the basis of qualifications, merit, and business need.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $350K-$420K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Trustly, 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. This role's midpoint ($385K) sits 76% above the category median. Disclosed range: $350K to $420K.
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.
Trustly AI Hiring
Trustly has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $315K - $420K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 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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