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About This Role
Company Description Are you ready to trade your job for a journey? Become a FlyMate!
Passion, excitement & global collaboration are all core to what it means to be a FlyMate. At Flywire, we’re on a mission to deliver the world’s most important and complex payments. We use our Flywire Advantage - the combination of our next-gen payments platform, proprietary payment network and vertical specific software, to help our clients get paid, and help their customers pay with ease - no matter where they are in the world.
What more do we need to truly be unstoppable? Perhaps, that is you!
Who we are:
Flywire is a global payments enablement and software company, founded more than a decade ago to solve high-stakes, high-value payments in higher education. We’ve since scaled into new regions and industry verticals and expanded our product offerings to deliver meaningful value to our clients around the world.
Today we support more than 5,300 clients across the global education, healthcare, travel & B2B industries, with diverse payment methods across 240 countries & territories and more than 140 currencies.
With over 1,400 global FlyMates, representing more than 40 nationalities, and in 15 offices world-wide, we’re looking for FlyMates to join the next stage of our journey as we continue to grow.
Job Description The Opportunity:
As the RevOps Specialist – AI & Proposals at Flywire, you’ll own a core part of our go-to-market engine by leading the end-to-end delivery of high-impact, competitive proposals for clients across our global verticals. You’ll also drive the modernization and scaling of our RFP motion using AI, automation, and process innovation. This is your chance to work with cutting edge tools from day 1, shape how Flywire operates, and see your ideas drive immediate impact in a fast-paced collaborative environment where your contributions matter.
You’ll gain deep product knowledge, client insights, and business acumen that accelerates your career trajectory. We’re looking for someone who’s excited to transform how we work, with a growth mindset and curiosity about AI and automation. If you thrive in fast-paced environments, want to build something from the ground up, and see this role as the start of something bigger, come join Flywire!
What You’ll Do:
- Own the end-to-end proposal process from RFP receipt through final submission, including coordination with cross-functional stakeholders, authoring responses, and ensuring consistency, quality, and brand alignment
- Manage the RFP tracker, timelines, and workflows to maintain clear visibility, proactive communication, and on-time delivery across all active proposals and internal SLAs
- Develop high-quality proposal content by understanding client requirements, crafting compelling win responses, and connecting Flywire’s product capabilities and value propositions to what clients are looking for
- Champion AI/Automation improvements across the RFP lifecycle; optimize our toolkit (Claude), identify opportunities to scale content and drafting workflows, and continually find ways to reduce manual effort and turnaround times
Build and enhance the RFP content library and identify emerging trends and patterns; use these insights to improve our response strategy, strengthen operational efficiency, and share learnings with the broader team *
Qualifications Here's what we're looking for:
- 1-3 years of experience in project coordination/management, business operations, proposal writing, or other client-facing roles
- Passion and genuine curiosity for leveraging AI/automation to transform how we work; comfort experimenting with tools like Claude to drive workflow optimization and efficiency
- A “figure it out” mentality with the ability to challenge assumptions and find better ways of working; interest in building something from the ground up
- Focused project manager with ability to successfully manage multiple deadlines and cross-functional stakeholders; willingness to quickly learn e-procurement platforms
- Analytical skills and the ability to learn from RFP trends; you’ll develop strategic thinking on the job with mentorship and real-world exposure
Exceptional content development and editing abilities with strong attention to detail and the skill to translate complex information into clear, compelling responses *
Additional Information Submit today and get started!
We are excited to get to know you! Throughout our process you can expect to meet different FlyMates including the Hiring Manager and other Flymates. Your Talent Acquisition Partner will walk you through the steps and be your “go-to” person for questions.
Flywire is an equal opportunity employer and follows a policy of administering all employment decisions and personnel actions without regard to race, color, religion, sex, pregnancy, gender identity, national origin, age, ancestry, physical or mental disability, sexual orientation, genetic disposition or carrier status, veteran status, or any other category protected under applicable national, federal, state or local law.
The US base salary range for this full-time position is $87,000-$110,000 and benefits. Our salary ranges are determined by role, position level, and location. The range displayed on this 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 several other factors, including job-related skills, experience, relevant education and training.
#LI-Hybrid
Salary Context
This $87K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Flywire, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($98K) sits 54% below the category median. Disclosed range: $87K to $110K.
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.
Flywire AI Hiring
Flywire has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Boston, MA. Compensation range: $110K - $110K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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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