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About This Role
Career Launch Program | ExpiTrans | Rancho Santa Margarita, CA Full-Time | Entry Level | In-Office $54,000–$80,000 Base + Uncapped Bonuses + Residual Income Opportunity
Start Your Career Where AI, Fintech and Business Development Meet
Graduating from college and looking for something more exciting than a traditional entry-level corporate job?
ExpiTrans is looking for ambitious, competitive and technology-driven recent graduates to join our Rancho Santa Margarita office and begin their careers in fintech business development.
This is an entry-level business development role for people who want to learn how to identify opportunities, build relationships, recruit partners and grow revenue while using the latest AI technology to work smarter and move faster.
You’ll work inside a fintech company operating across payments, software, banking, AI and commerce.
This is not a job where you spend two years waiting to do meaningful work.
You’ll be expected to contribute from day one.
And unlike many entry-level positions, your earning potential does not have to stop at your salary.
In addition to a competitive base, this role includes opportunities for uncapped performance bonuses and ongoing residual compensation tied to qualifying business you help develop.
What You'll Actually Do
You will help ExpiTrans identify, engage and develop new business relationships.
Your work may include:
- Prospecting and identifying potential business partners, agents, referral relationships and strategic opportunities.
- Conducting outbound calls, email outreach and LinkedIn engagement.
- Recruiting new payment partners and developing relationships with prospective partners.
- Using ChatGPT, Claude and other AI tools to research prospects, industries and opportunities.
- Using AI to improve sales preparation, personalization, follow-up and productivity.
- Building and managing prospecting lists and outbound sales campaigns.
- Conducting discovery conversations with prospective partners.
- Learning how to uncover real business problems and connect them to solutions.
- Qualifying opportunities and advancing them through the sales pipeline.
- Following up consistently and professionally.
- Researching new industries, verticals and partnership channels.
- Helping launch new fintech products and go-to-market initiatives.
- Analyzing prospecting activity, conversion rates and pipeline performance.
- Working alongside experienced business development, partner, product and operations leaders.
You’ll learn how modern business development works when AI, data, technology and human relationships are combined.
Who We're Looking For
You may be a great fit if you:
- Recently graduated or are about to graduate from college.
- Have completed prior sales training, with B2B sales training strongly preferred.
- Have exposure to professional selling through a university sales program, internship, sales organization, SDR/BDR training program, previous job or comparable structured training.
- Understand basic sales concepts such as prospecting, discovery, qualification, objection handling, follow-up and pipeline management.
- Are comfortable picking up the phone and starting conversations with people you don't know.
- Are genuinely interested in AI, fintech and emerging technology.
- Already use AI tools to make yourself faster, better prepared or more effective.
- Are competitive and motivated by measurable results.
- Communicate confidently and professionally.
- Learn quickly without needing everything spelled out.
- Are resilient and don't get discouraged by rejection.
- Want responsibility early in your career.
- Are interested in understanding how businesses grow.
- Like the idea of building relationships that can create value over time.
Prior professional B2B selling experience is a plus, but not required. Prior sales training is required.
Your degree can be in business, finance, economics, marketing, communications, technology or another field.
What matters most is your ability to learn, communicate, sell and execute.
Particularly Strong Candidates
We are especially interested in graduates who participated in:
- Collegiate sales programs
- Professional selling courses
- Sales competitions
- Business development internships
- SDR or BDR internships
- Entrepreneurial programs
- Student-run businesses
- B2B sales training programs
- Business or marketing organizations
If you've already learned how to prospect, conduct discovery, handle objections or manage a sales process, we want to hear about it.
If you've also figured out how to use AI to make yourself better at those things, even better.
You Don't Need
You do not need previous:
- Payment processing experience
- Banking experience
- Fintech experience
- Professional AI experience
- Years of professional sales experience
We can teach you fintech.
We can teach you payments.
We can help you become dramatically better with AI.
We don't want to start from zero on your understanding of professional selling.
Why ExpiTrans?
ExpiTrans operates at the intersection of payments, banking, software, AI and commerce.
That gives an ambitious new graduate exposure to industries and disciplines that would normally require several different jobs to experience.
You'll learn how businesses are identified, approached, won, onboarded and developed into long-term relationships.
You'll also see firsthand how AI is changing prospecting, research, sales preparation, automation, customer engagement and the way modern companies operate.
And because this is business development in the payments industry, successful relationships can continue creating value long after the initial sale.
That means high performers have the opportunity not only to earn bonuses for producing results today, but also to begin building ongoing residual income from qualifying business they help originate.
Compensation & Upside
Target base salary: $54,000–$80,000 annually, depending on experience, capabilities and demonstrated sales and AI proficiency.
The base salary is only one part of the opportunity.
Qualified team members may also earn:
- Uncapped performance bonuses
- Business development incentives
- Ongoing residual compensation on qualifying business
- Incentives tied to successful partner recruitment and business growth
- Expanded earning opportunities as responsibilities and portfolio ownership increase
The goal is simple:
As the value you create grows, your opportunity should grow with it.
For an ambitious person beginning a career in sales, payments or fintech, this creates an unusual combination:
Competitive base salary + uncapped bonuses + recurring residual income opportunity + long-term career advancement.
All incentive and residual compensation is subject to the applicable compensation plan and qualification requirements.
Career Path
This role is designed as a career starter.
A potential path may include:
Business Development Associate → Senior Business Development → Partner Recruiter → Partner Relationship Manager → Channel Manager → Strategic Partnerships → Sales Leadership
High performers may also have opportunities over time to move into areas such as:
- Product
- AI and automation
- Fintech operations
- Data and analytics
- Strategy
- Leadership
We want this position to be a launching point, not a destination.
The Kind of Person We Want
During the interview, we'd rather hear about:
- Sales training you've completed.
- A time you persuaded someone or won an opportunity.
- A sales competition or business project you participated in.
- Something interesting you've built or accomplished using AI.
- A difficult conversation you handled successfully.
- Something you taught yourself without being required to.
- A business idea you've been thinking about.
- A time you competed for something and really wanted to win.
We aren't looking for ten years of experience.
We're looking for evidence of potential, competitiveness, curiosity and coachability.
Location
This role is based in our Rancho Santa Margarita, California office and is designed as an in-person career-development opportunity.
We're especially interested in talented recent graduates and early-career candidates from Orange County and the surrounding area who want to build their careers locally.
How to Stand Out
Don't just send us the same résumé you're sending everywhere else.
Tell us:
1. What sales training have you completed?
2. What's the coolest thing you've done with AI?
3. Tell us about a time you had to persuade someone, compete or win an opportunity.
4. If we gave you a list of 100 potential businesses or partners to approach, how would you decide where to start?
5. What do you want your career to look like three years from now?
We're looking for people who are unusually curious, competitive, resourceful and hungry to grow.
If that sounds like you, we'd like to meet you.
Pay: $54,000.00 - $150,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Cell phone reimbursement
- Health insurance
- Paid time off
Work Location: In person
Salary Context
This $54K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ExpiTrans, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($102K) sits 52% below the category median. Disclosed range: $54K to $150K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
ExpiTrans AI Hiring
ExpiTrans has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rancho Santa Margarita, CA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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