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The Opportunity
FutureSight is seeking a Co\-Founder \& CEO to lead a new AI\-native reverse logistics orchestration venture. This is a co\-founder partnership with meaningful founder equity, not a salaried executive role.
Reverse logistics — everything that happens after a sale — has become one of the largest and least controlled cost centers in modern commerce. Returns, warranties, exchanges, and recoveries are unavoidable, high\-volume workflows, yet they remain managed through fragmented tools, spreadsheets, and manual judgment. We are building an AI\-first orchestration layer that sits between customers, fulfillment networks, and finance systems. The platform enforces policy at intake, captures evidence at the source, determines the optimal path for each return — recovery, resale, exchange, credit, refurbishment, or return to inventory — and tracks outcomes through to financial closure. Rather than replacing existing ERPs, WMSs, or commerce platforms, the platform is designed to operate as the orchestration layer across them, enabling organizations to actively control cost, risk, and recovered value after the sale.
We are now seeking the founder to lead the venture from here.
Market Context* Global reverse logistics activity is estimated at $700B–$900B annually, with projections exceeding $1T by 2030
- US consumers returned roughly $743B of merchandise in 2023, accounting for 14–18% of total retail sales
- In e\-commerce\-heavy categories, return rates routinely exceed 25–40%, versus approximately 9–10% for in\-store purchases
- Processing a single return typically costs 20–65% of the item's value, and for many operators, returns consume 20%\+ of order value while tying up working capital for 30–90 days before financial closure
- Rising return volumes, traceability expectations, and sustainability pressure are converging to make this a systems problem, not a policy one
About FutureSight
FutureSight is a leading venture builder that co\-creates world\-class software companies with values\-driven entrepreneurs from inception to exit. We are a team of founders, operators and designers with experience successfully bringing software to market at scale.
You’ll co\-create with a proven studio team, led by John Carbrey (4x entrepreneur, $100M ARR), Krista LaRiviere (3x exited, E\&Y Top Women Entrepreneur), Alan Smith (Strategyzer co\-founder, $120M in products built), Prathna Ramesh (former MD of Maple Leaf Angels, $275M in follow\-on capital), and Johnny Tong (0\-to\-1 builder, acquired by SAP and Stripe) bring a rare combination of operator exits, institutional investing, and AI product depth.
The Partnership* Founder equity with meaningful ownership from inception
- Pre\-seed capital committed by FutureSight for early hires and MVP development, with potential for follow\-on funding
- Venture building resources, including embedded design, engineering, growth, and fundraising support from day one
- Investor and advisor network across vertical AI and early\-stage capital markets
- A true co\-creation model in which you operate as CEO with FutureSight's cross\-functional team as your partner
What you'll own
As Co\-Founder \& CEO, you will set the venture's direction and lead its execution.
- Strategy — Refine the ICP, pricing model, and product positioning, including the choice of execution path between consumer returns–led (B2C commerce) and warranty \& credit–led (B2B supply chains)
- Customer Development — Lead pilots with operations\-heavy commerce, retail, or supply chain organizations, convert them to paid engagements, and build the go\-to\-market motion
- Product — Partner with the FutureSight product and engineering team to ship V1 and iterate on user feedback, with a focus on intake intelligence, policy enforcement, dynamic routing, and financial closure
- Capital — Lead the seed raise, supported by FutureSight's network and traction
- Team — Recruit and lead the founding team, and establish the cultural foundation of the company
Co\-Founder Profile* Previous founding experience at a venture\-backed company
- Demonstrated success in B2B SaaS go\-to\-market, including sales and customer engagement
- Fundraising fluency, with the ability to develop investor narratives and close capital
- Proven ability to attract, develop, and retain top talent
- Clear\-eyed understanding of the risks and demands of co\-founding a venture\-backed company
- Direct exposure to operations\-heavy commerce, supply chain, or logistics environments, with hands\-on experience in returns, warranty, fulfillment, or post\-sale workflows, and familiarity with AI agents/LLMs
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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
FutureSight AI Hiring
FutureSight has 9 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Los Angeles, CA, US, US, New York, NY, US. Compensation range: $850K - $850K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 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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