Remote Founding AI Engineer Jobs
Remote Founding AI Engineer jobs are open across the U.S. in AI-native startups, deep-tech venture-backed companies, and distributed product teams building at the frontier of machine learning and large language model applications. Companies like Featherless AI, WORG, and HEARD are actively hiring founding ai engineers remotely, from early-career contributors to senior technical leads who set architecture direction from day one. Find a role that fits below and apply directly.
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See related jobsProvn is helping Envorso Sports recruit a Founding AI Engineer. Please apply on our home page on Provn and take the challenge to stand-out from competition.
About Envorso Sports
Envorso Sports is building the platform that runs a professional sports team's entire fan-facing business: ticketing, website, mobile app, and increasingly commerce, CRM, and marketing, from one system instead of six disconnected vendors.
Today, most teams run a fragmented stack: a website vendor, a ticketing provider, a separate mobile app, CRM tools, marketing automation, and game-day engagement solutions bolted together. Teams don't want to pay five vendors and contractors to stitch the data together. That creates a disconnected fan journey and leaves revenue on the table.
Our platform is live today with the Seattle Seawolves of Major League Rugby, and the league itself is now on the platform, with individual teams onboarding through the season. Ticketing, website, and mobile app are shipped and in production with paying customers. CRM, marketing automation, personalization, and revenue operations are what we build next, AI-native from the start. We want candidates to know exactly where the product stands before they join.
About the Founding Engineer Role
You'll be our first in-house engineer. This is a founding role: you'll own the fan data platform for the long term and help shape how we grow engineering, not ship one feature and move on.
The problem is simple to state. Our teams and the league sit on a large fan database: ticket transactions, web and app activity, shop purchases, and email engagement. Today almost none of it is used. One salesperson calls season-pass holders; one marketer sends a broadcast email and sees who bites. Your job is to turn that data into revenue: unify what we know about each fan across ticketing, web, app, shop, and email; decide which fans should get which offer; and deliver it through email and in-app offers or push.
Offers are concrete: a promo code for an abandoned cart, a season pass or match pass, a merch bundle, a seat upgrade. Getting the wrong offer to the wrong fan costs real money and real trust with a small, loyal fan base.
You'll build for a three-to-five-year horizon, not just this quarter. We need someone who thinks like an architect (data model, scale, failure handling, consent, cost) while still shipping something a team can use in weeks.
You'll also be the engineer closest to our customers' front offices. Part of the job is understanding how a team's revenue and marketing operations actually work, and helping decide what we build first based on impact and effort.
You'll report to Gopal Rao and work alongside our five-engineer team in Argentina as one small group. Expect real overlap-hours collaboration, and expect to take a problem, make the call, and ship without waiting on someone else.
What You'll Do
Own the fan data platform end to end: a unified fan record across ticketing, web, app, and email, designed to hold up as we grow from one team's list to a whole league's
Ship fan segmentation and a targeted offers engine that decides which fans get which offer, and delivers it by email reactivation and in-app offers or push
Build marketing ops and revenue ops driven by agentic AI: fan identity, segments, campaign builder and automation, and propensity, churn, and pricing models
Bring commerce into the fan record: shop purchases alongside tickets, so offers can cross-sell merch, passes, and seat upgrades
Build the surfaces people use, in TypeScript and Next.js on web and in our mobile app, so offers are something a marketer reviews and a fan acts on, not just an API response
Design for production from day one: consent and opt-in enforcement, no duplicate sends, failures that are visible instead of silent, and costs you can explain
Work with team front offices to understand their revenue goals, and sequence the roadmap by impact and effort
Make AI how we ship: build an AI-native product where models change the outcome for a marketer or a fan, and run an agentic development process (AI-SDLC) with coding agents, specs and tests as guardrails, and human review before anything reaches a fan
Help decide how engineering grows: how we scale with our Argentina team, what we build in-house, and who we hire next
How We Hire
Apply through Provn by completing a short challenge and video walkthrough. If your submission clears the bar, you'll move straight to a conversation with our team. We don't wait to batch candidates. The process: a screen with Gopal, a technical interview, then a panel with engineering and product leaders.
Work Authorization
Must be authorized to work in the US. No employer-sponsored visas for this role.
Provn is helping Envorso Sports recruit a Founding AI Engineer. Please apply on our home page on Provn and take the challenge to stand-out from competition.
About Envorso Sports
Envorso Sports is building the platform that runs a professional sports team's entire fan-facing business: ticketing, website, mobile app, and increasingly commerce, CRM, and marketing, from one system instead of six disconnected vendors.
Today, most teams run a fragmented stack: a website vendor, a ticketing provider, a separate mobile app, CRM tools, marketing automation, and game-day engagement solutions bolted together. Teams don't want to pay five vendors and contractors to stitch the data together. That creates a disconnected fan journey and leaves revenue on the table.
Our platform is live today with the Seattle Seawolves of Major League Rugby, and the league itself is now on the platform, with individual teams onboarding through the season. Ticketing, website, and mobile app are shipped and in production with paying customers. CRM, marketing automation, personalization, and revenue operations are what we build next, AI-native from the start. We want candidates to know exactly where the product stands before they join.
About the Founding Engineer Role
You'll be our first in-house engineer. This is a founding role: you'll own the fan data platform for the long term and help shape how we grow engineering, not ship one feature and move on.
The problem is simple to state. Our teams and the league sit on a large fan database: ticket transactions, web and app activity, shop purchases, and email engagement. Today almost none of it is used. One salesperson calls season-pass holders; one marketer sends a broadcast email and sees who bites. Your job is to turn that data into revenue: unify what we know about each fan across ticketing, web, app, shop, and email; decide which fans should get which offer; and deliver it through email and in-app offers or push.
Offers are concrete: a promo code for an abandoned cart, a season pass or match pass, a merch bundle, a seat upgrade. Getting the wrong offer to the wrong fan costs real money and real trust with a small, loyal fan base.
You'll build for a three-to-five-year horizon, not just this quarter. We need someone who thinks like an architect (data model, scale, failure handling, consent, cost) while still shipping something a team can use in weeks.
You'll also be the engineer closest to our customers' front offices. Part of the job is understanding how a team's revenue and marketing operations actually work, and helping decide what we build first based on impact and effort.
You'll report to Gopal Rao and work alongside our five-engineer team in Argentina as one small group. Expect real overlap-hours collaboration, and expect to take a problem, make the call, and ship without waiting on someone else.
What You'll Do
Own the fan data platform end to end: a unified fan record across ticketing, web, app, and email, designed to hold up as we grow from one team's list to a whole league's
Ship fan segmentation and a targeted offers engine that decides which fans get which offer, and delivers it by email reactivation and in-app offers or push
Build marketing ops and revenue ops driven by agentic AI: fan identity, segments, campaign builder and automation, and propensity, churn, and pricing models
Bring commerce into the fan record: shop purchases alongside tickets, so offers can cross-sell merch, passes, and seat upgrades
Build the surfaces people use, in TypeScript and Next.js on web and in our mobile app, so offers are something a marketer reviews and a fan acts on, not just an API response
Design for production from day one: consent and opt-in enforcement, no duplicate sends, failures that are visible instead of silent, and costs you can explain
Work with team front offices to understand their revenue goals, and sequence the roadmap by impact and effort
Make AI how we ship: build an AI-native product where models change the outcome for a marketer or a fan, and run an agentic development process (AI-SDLC) with coding agents, specs and tests as guardrails, and human review before anything reaches a fan
Help decide how engineering grows: how we scale with our Argentina team, what we build in-house, and who we hire next
How We Hire
Apply through Provn by completing a short challenge and video walkthrough. If your submission clears the bar, you'll move straight to a conversation with our team. We don't wait to batch candidates. The process: a screen with Gopal, a technical interview, then a panel with engineering and product leaders.
Work Authorization
Must be authorized to work in the US. No employer-sponsored visas for this role.
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Who's Hiring
What Employers Look For
The qualifications that appear most often in remote founding AI engineer jobs.
- Strong proficiency in Python and experience building production machine learning systems
- Hands-on experience with large language model fine-tuning, evaluation, and deployment
- Familiarity with MLOps tooling and cloud infrastructure on AWS, GCP, or Azure
- Experience designing and maintaining data pipelines for model training and inference
- Ability to work independently and make architectural decisions with limited direction
- Bachelor's or master's degree in computer science, machine learning, or a related field
Tips for Your Remote Founding AI Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote founding ai engineer openings from across the U.S. in one place, so you can find roles that match your skills and apply directly without sorting through listings that aren't actually remote or aren't a real founding engineer scope.
Show your async technical communication
Remote founding ai engineer hiring teams read written design docs, architecture decisions, and async Slack threads before they ever meet you. Keep a public engineering blog or GitHub repo where your reasoning on technical tradeoffs is visible and readable before your first interview.
Build a portfolio of shipped AI systems
Remote employers can't watch you work, so your past builds do the talking. Document deployed LLM applications, fine-tuning experiments, or production inference pipelines with real outcomes, not just notebooks, so hiring teams can evaluate your technical depth independently.
Prepare for async-first remote interviews
Many remote AI startups use written take-homes, recorded video responses, or async technical reviews before live calls. Practice explaining your architectural choices in concise written form, and be ready to walk through past AI system decisions in a Loom or written brief.
Remote Founding AI Engineer Jobs: Frequently Asked Questions
How do I get a remote founding ai engineer job?
Remote founding ai engineer roles go to candidates who can operate with minimal oversight and communicate complex technical decisions clearly in writing. Remote-first startups and distributed AI teams screen for self-direction, strong async communication, and hands-on experience with LLM pipelines, fine-tuning, or production ML systems. Building a public portfolio of AI projects, writing about your technical decisions, and contributing to open-source AI tooling all signal the kind of independent ownership remote employers need in a founding engineer.
Which companies hire remote founding ai engineers?
Remote founding ai engineer roles are posted by Featherless AI, WORG, and HEARD and others right now, based on current remote listings on Migrate Mate as of November 2026. The companies hiring most actively tend to be remote-first AI startups, venture-backed product teams, and enterprise software companies building out early AI functions across sectors like developer tooling, healthcare AI, and fintech.
Can you get a remote founding ai engineer job with no experience?
Yes, but remote founding ai engineer roles are harder to land without experience because you're expected to work independently and move fast without in-person guidance. Entry paths that work include building and publishing AI side projects, contributing to open-source LLM frameworks, or completing contract AI work you can point to. Early-stage startups are more likely to take a chance on strong project evidence than larger distributed teams with more structured hiring.
Do you need a degree for remote founding ai engineer jobs?
Not always. Remote employers hiring founding ai engineers weigh demonstrated technical ability heavily, and a strong portfolio of shipped AI systems, published models, or real-world LLM deployments can open doors that a degree alone cannot. That said, some companies, especially those with enterprise clients or regulated industries, do prefer a computer science or machine learning background, so a degree still helps in competitive candidate pools.
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