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About This Role
About Us
We are an established Independent Sales Organization with seven years of experience in the credit-card-processing and merchant-services industry. We understand how merchants evaluate processors, why they consider switching, the objections they raise and the factors that ultimately drive conversion.
We are building a proprietary, AI-driven lead-intelligence platform to identify and prioritize merchants most likely to become customers. Our goal is to combine our industry knowledge, current sales-call recordings, operational information and external business data into a system that makes prospecting more scientific, targeted and measurable.
We are seeking an entrepreneurial data-science leader to design and lead this initiative from research and prototype development through prospective testing and commercialization.
The Opportunity
This is not a conventional data-science position in which a completed dataset and predefined model are handed to you. You will help determine what data matters, how it should be collected and labeled, and how qualitative sales intelligence can be transformed into quantitative predictions.
Because our historical labeled data is limited, the initial work will include analyzing current sales-call recordings, interviewing members of our sales organization and identifying the characteristics, circumstances and behaviors associated with merchants who are receptive to switching processors.
The successful candidate will build an initial lead-scoring methodology, test it against real-world sales results and continuously improve it as new outcome data is generated.
This is a founding-level opportunity with salary and equity participation in the resulting platform. The right candidate will have substantial autonomy and the opportunity to create a potentially valuable commercial product within an established operating business.
Responsibilities
- Develop the overall research, data and AI strategy for a proprietary merchant lead-intelligence platform.
- Analyze recorded sales conversations using transcription, natural-language processing, large language models and human-reviewed qualitative research.
- Create a structured framework for extracting relevant information from calls, including merchant characteristics, processing concerns, switching triggers, objections, urgency, decision-maker engagement and sales outcomes.
- Interview sales representatives and company leadership to capture domain knowledge that may not appear in existing structured data.
- Design reliable labeling and quality-control procedures for call transcripts and sales outcomes.
- Identify the signals most likely to predict meaningful sales progression, including statement submissions, follow-up meetings, proposals and signed merchant accounts.
- Develop an initial transparent, rules-based scoring model suitable for use while labeled data remains limited.
- Progressively develop statistical and machine-learning models as sufficient prospective data becomes available.
- Identify and evaluate external data sources that could reveal merchant size, growth, industry, number of locations, technology, ownership changes, hiring activity and other relevant characteristics.
- Connect internal conversation insights to externally observable business signals that can be used to identify new prospects.
- Design controlled sales experiments comparing AI-selected leads with leads selected through our existing process.
- Establish appropriate performance metrics, including conversion lift, precision among top-ranked leads, customer-acquisition cost and expected merchant profitability.
- Work with sales leadership to integrate lead recommendations into the company’s existing workflow.
- Build or manage the development of an initial working prototype and subsequent production platform.
- Develop appropriate documentation, data-governance practices and safeguards for sensitive sales and merchant information.
- Help evaluate and recruit additional data, engineering or research talent as the project grows.
- Communicate findings and recommendations clearly to business leadership and nontechnical sales personnel.
Required Qualifications
- Advanced training or equivalent professional experience in data science, applied statistics, machine learning, computer science, econometrics, quantitative marketing or a closely related field.
- Demonstrated experience with predictive modeling, propensity modeling, lead scoring, customer analytics or another classification/ranking problem.
- Strong Python and SQL capabilities.
- Experience working with unstructured text, call transcripts, conversation analytics or natural-language processing.
- Familiarity with large language models and their use in structured information extraction, classification and research workflows.
- Ability to work effectively with limited historical data and develop a disciplined data-collection and validation plan.
- Understanding of experimental design, prospective validation, model evaluation and common problems such as data leakage, overfitting and selection bias.
- Ability to combine qualitative business research with quantitative analysis.
- Strong product judgment and an interest in building a commercial platform, not merely producing an academic model or research report.
- Ability to work independently, define priorities and communicate directly with company leadership.
- High standards for confidentiality, security and responsible handling of recorded conversations and merchant information.
Preferred Qualifications
- Experience in merchant acquiring, credit-card processing, payments, fintech, banking or financial services.
- Experience analyzing B2B sales calls or developing sales-intelligence, revenue-operations or customer-acquisition systems.
- Familiarity with merchant categories, processing statements, pricing structures, interchange, chargebacks, point-of-sale systems and the reasons merchants change processors.
- Experience with speech-to-text systems, embeddings, retrieval systems and LLM evaluation.
- Experience combining internal company data with licensed or publicly available external business data.
- Experience taking a data product from initial research through deployment and ongoing monitoring.
- Previous startup, founding-team or entrepreneurial experience.
- Experience managing technical contributors or outside development resources.
Initial Objectives
During the first several months, the successful candidate will be expected to:
- Audit the information currently available, including sales-call recordings, CRM information and business outcomes.
- Interview company leadership and high-performing sales representatives.
- Develop a taxonomy for merchant needs, dissatisfaction, switching triggers, objections and levels of sales progression.
- Build and validate an AI-assisted process for transcribing, reviewing and structuring sales conversations.
- Produce an initial rules-based lead-scoring system based on qualitative and quantitative evidence.
- Recommend external data sources and determine which observable merchant characteristics can support prospect identification.
- Design a prospective experiment comparing prioritized leads with the company’s current lead-selection process.
- Deliver a working prototype and a roadmap for developing a defensible production platform.
What Success Looks Like
Success will not be measured by producing an impressive demonstration or using the newest AI technology. It will be measured by whether the system consistently helps our sales organization:
- Reach more qualified decision-makers.
- Identify merchants with genuine reasons to reconsider their current processor.
- Obtain more processing statements and substantive follow-up meetings.
- Generate more proposals from the same amount of sales activity.
- Improve close rates and merchant profitability.
- Explain why particular leads are being prioritized.
- Learn and improve as additional sales outcomes are generated.
Why Join Us
- Join an established ISO with seven years of merchant-services experience and real industry knowledge.
- Lead a strategically important initiative with direct access to company decision-makers and sales personnel.
- Build a proprietary AI and data product around an identifiable commercial need.
- Receive a competitive salary together with meaningful equity participation.
- Work remotely with substantial autonomy.
- Have the opportunity to develop the project into a broader commercial platform.
Salary
Candidates will be paid a competitive salary based on experience. Expected pay range is $70-80,000 per year. This position is full time and eligible for full benefits package as well.
Salary Context
This $70K-$80K 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 Paynada, 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 ($75K) sits 65% below the category median. Disclosed range: $70K to $80K.
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.
Paynada AI Hiring
Paynada has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $80K - $80K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 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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