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The Opportunity
Finance teams at B2B companies lose cash the same way every month: an invoice ages past 45 days, someone sends an email, no reply, someone calls, voicemail, they call again next week, still nothing. AR teams spend 60–70% of their week on manual follow\-up instead of posting cash, closing disputes, or analyzing aging. Adding headcount doesn't fix it; manual follow\-up simply doesn't scale, and every ERP hits the same wall.
Problem and solution validation are complete. Live pilots are running with early design partners and entering production evaluation. The next phase is proving in\-production reliability, delivery economics, and scalability. The teams that ship credible AI workers into finance\-sensitive functions in the next 18 months will define the category. We intend to be one of them.
The Venture
Argentl delivers an AI collections agent that handles outbound debtor follow\-up automatically — trained, monitored, and continuously improved by our team. Rather than another self\-serve SaaS tool, Argentl is evaluated on outcomes: DSO reduction, hours returned to the AR team, and cash commitments captured — not configuration effort.
Give us a list of overdue accounts and our voice agent calls each debtor, confirms invoice awareness, and captures exactly what they say — a payment commitment, a dispute, a request to split, or silence. The AR team gets a clean morning report: who's committed, who's disputing, who's gone silent, and what to do next. No ERP migration, no system credentials, no workflow overhaul — we're calling within a week of receiving the data. The roadmap expands from collections follow\-up into adjacent AI finance\-ops roles, all delivered through the same managed model.
The Partnership
The CEO co\-founder is in place. The customer thesis is validated, design partners are engaged, and live pilots are running. What remains is to bring on the technical co\-founder — a partner who will own product and engineering with the same conviction, urgency, and ownership stake.
This is a co\-founder role, not a CTO hire. In return for that level of commitment, you receive founder\-level equity, founder\-level authority with co\-decision rights on product, technology, hiring, fundraising, and strategy, and a genuine partnership with the CEO on every material decision.
What you'll own
As Co\-Founder \& CTO, you will take over the production code and continue building the product (the first lines of production code), set the technical direction, and lead execution end\-to\-end.
- Architecture \& Reliability — Harden the voice and workflow systems for in\-production reliability. Establish core architecture, observability, and audit\-trail standards for a product that speaks to customers' debtors on their behalf.
- Delivery Layer — Build the internal infrastructure that lets the team configure, monitor, and optimize agent performance without scaling engineering headcount. Mature the workflow engine so every call, intent classification, and next\-action flows through reliably.
- Integrations \& Distribution — Own the accounting and ERP integration layer (QuickBooks, Xero, and beyond) that turns Argentl from a CSV upload into an embedded part of the finance stack — the primary distribution unlock.
- Unit Economics — Own the trade\-offs between AI quality and cost\-per\-call, and lock in unit economics within a productized monthly pricing model.
- Customer \& Market — Represent the company in front of finance leaders, partners, and investors as the technical voice of the venture.
- Roadmap — Convert pilots into paying customers, expand the AI staff beyond collections, and begin a path toward custom and self\-hosted models where data\-privacy and financial\-compliance requirements demand it.
- Team — Recruit and lead the founding engineering team, and establish the cultural foundation of the company
Co\-Founder ProfileMust Have:
- Shipped LLM\-powered and voice systems into production environments where incorrect outputs carried real consequences
- Full\-stack confidence across applied LLMs, real\-time voice and conversational AI, telephony, human\-in\-the\-loop workflows, observability, and secure multi\-tenant SaaS
- Well\-formed convictions about reliability engineering, edge\-case management, and the trade\-offs between AI quality and cost\-per\-call
- Experience building managed and service\-heavy platforms, with the discipline to scale delivery without scaling engineering headcount
- Prepared to operate as a principal — including saying "this won't scale" or "this isn't ready" when the integrity of the product requires it
- A hands\-on builder — you still write code and ship, and you're happiest close to the product
Great to Have:
- Finance, accounting, or FinOps background — you understand AR, DSO, aging, disputes, and how finance teams actually work
- Based in Los Angeles or the San Francisco Bay Area (or ready to be)
- Pragmatic and creative — a solution finder who gets to "working" fast and iterates from there
- Co\-operative, strategic, and hands\-on in equal measure
- Capable and willing to help in finding and closing early customer partnerships
- An active social media presence you can put to work for founder\-led distribution
How to Apply
Please submit your resume, LinkedIn profile, and a brief note on why this venture aligns with your goals as a founder. We will move quickly for the right candidate.
FutureSight is committed to diversity, equity, and inclusion. We welcome applicants of all backgrounds and experiences.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FutureSight, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FutureSight AI Hiring
FutureSight has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $850K - $850K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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