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Eragon

Agentic AI operating system that replaces your entire software stack with a prompt

4 open rolesSeed Β· $12M Seed
size
β€”
backers
LJVSCAP+2
hq
San Francisco
industry
AI

Open roles at Eragon

4 positions we're tracking, re-checked daily

  • ML Engineer

    san franciscoMid-levelfirst seen 6 months ago
    apply β†—
  • AI PM

    san franciscoMid-levelfirst seen 6 months ago
    apply β†—
  • AI UI/UX Designer

    san franciscoMid-levelfirst seen 6 months ago
    apply β†—
  • Applied Research Engineer

    san franciscoMid-levelfirst seen 6 months ago
    apply β†—
tracking Eragonlast checked October 2, 2026

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A role stays uncontested for about four days. Here's the window, and where we put you in it.

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  1. livewatching Eragon0 applicants
  2. +2hrole spotted & verified1
  3. +4hyou get the alert1
  4. day 3you've applied~8
  5. day 7+hits the job boards250+
the product

What Eragon is building

Eragon is betting that enterprise software as we know it - buttons, dashboards, pull-down menus - is about to be replaced by a prompt. The product is an agentic AI operating system for businesses: instead of logging into Salesforce to check your pipeline, or Tableau to pull a report, or Jira to assign a task, you ask Eragon. It post-trains open-source models (Qwen, Kimi) on a company's own data and infrastructure, and deploys them inside the company's cloud environment, so the model weights and data never leave the client's servers. The demo involves onboarding a new enterprise customer entirely by prompt: Eragon automatically provisions credentials, spins up a new instance, and starts an onboarding workflow, all from a single natural language instruction.

the money

Why this matters

Josh Sirota spent years in go-to-market at Oracle and Salesforce implementing enterprise software for large organisations, and his core insight is that the interface itself has become the bottleneck. The average knowledge worker touches six to ten different SaaS tools per day, each with its own learning curve, data model, and UI. Eragon's thesis is that AI is finally capable enough to collapse that complexity into a conversational layer that knows the company's data and can act on it. The company-data-stays-in-your-cloud angle is strategically important: Nvidia's Jensen Huang made nearly the same argument at GTC last week, describing how "every SaaS company will become Agentic as a Service" and how enterprise AI will require local, bespoke deployment. That validation from the most important infrastructure company in AI is a significant tailwind. Eragon already has customers including Corgi, an insurance startup that raised $180M post-YC, whose CEO called it "the best applied AI for enterprise in the market" which is a credible reference from a credible source, not a PR puff quote.

Investors: Long Journey Ventures (Arielle Zuckerberg), Soma Capital, Axiom Partners, Angels: Mike Knoop (Make/Integromat co-founder), Elias Torres

Working at Eragon

Eragon is a AI company based in San Francisco. Working at a AI company at this stage means opportunity to shape your role based on the company stage.

San Francisco is where most Eragon positions are located.

How to actually get hired at Eragon

Why applying the normal way doesn't work

Eragon runs an applicant tracking system, but hiring managers still work referrals first. A cold application to Eragon isn't dead, it's just fourth in line behind internal referrals, sourced candidates and recruiter pipelines.

Who to contact at Eragon

Who decides
the hiring manager
Best channel
LinkedIn or direct email

What to show them

Eragon post-trains open-source models (Qwen, Kimi) on customer-specific datasets and deploys them in isolated cloud environments. That's not a wrapper, it's a fine-tuning and deployment pipeline that needs to scale across multiple enterprise customers simultaneously, each with different data, different security requirements, and different model behaviour needs. This is the core technical work of the product. Core skills: Python, PyTorch or JAX, LLM fine-tuning (LoRA, QLoRA, full fine-tuning), model evaluation, distributed training, cloud deployment (AWS/GCP/Azure), enterprise data pipelines, model serving infrastructure. Proof of work: Fine-tune a small open-source model (Qwen-7B or equivalent) on a publicly available enterprise dataset, customer support logs, financial documents, or sales data, and write a 2-page technical memo documenting: what you did, what improved, what didn't, and how you'd adapt this pipeline for multi-tenant enterprise deployment where each customer's data must stay isolated. Send the GitHub repo and the memo together.

A cold email that works at Eragon

Subject: ML Engineer, [your one-line proof]
Hi Josh, I read the TechCrunch piece this morning. The post-training-per-customer architecture is the right call for enterprise data sovereignty. I fine-tuned Qwen on [specific dataset] last month, hit [X result], and wrote up what the multi-tenant isolation problem looks like from an ML infrastructure angle. Repo and memo attached, worth 15 minutes if the approach resonates?
Get personalised templates β†’

What Eragon screens for

The TechCrunch article published yesterday described Eragon's team as Sirota plus two technical co-founders: Rishabh Tiwari (Berkeley CS PhD candidate) and Vin Agarwal (MIT PhD). That's three people with $12M in the bank, live enterprise customers, and four open roles that nobody knows about yet because the announcement dropped this morning. The careers page was updated four days before the funding announcement, meaning they opened these roles quietly before going public, and the listings haven't propagated anywhere yet. Axiom Partners explicitly described Eragon as "the connective tissue for how modern teams operate and make decisions", that language maps directly to integration work, data pipelines, and enterprise sales infrastructure hires. Mike Knoop co-founded Make (formerly Integromat), the workflow automation platform that competes with Zapier, his investment signals conviction that Eragon can own the enterprise orchestration layer, and his portfolio experience pushes these companies toward deep integration work. Arielle Zuckerberg at Long Journey Ventures backs founders with enterprise GTM experience and pushes hard on customer success and expansion infrastructure. Soma Capital's portfolio companies at this stage typically need product and design talent urgently. The four roles on the careers page, ML Engineer, AI PM, AI UI/UX Designer, Applied Research Engineer, cover the full stack of what a 3-person technical team with live enterprise deployments urgently needs.

A tailored CV beats a generic one. Use Eragon's job description language to clear filters.

avoid

Don't make these mistakes

Avoid: The announcement dropped this morning, which means a wave of generic "congrats on the raise, I'd love to chat about opportunities" messages is already building in Josh's LinkedIn inbox. Stand out by making contact today, before that wave peaks, and leading with something specific and concrete, a proof of work, a specific observation about the product, or a short technical memo. Josh has a GTM background and evaluates people on their ability to think in specifics about customer problems. Generic AI enthusiasm does nothing here. If you are a designer or PM, show you understand the enterprise trust problem in agentic UI. If you are an engineer, show you understand the multi-tenant fine-tuning architecture.

Mistakes that kill Eragon applications

  • Generic CVs stand out at a -person company β€” and not in a good way. Fastest path to rejection.

  • Don't open with what you want. Open with what Eragon is dealing with right now β€” Josh Sirota spent years in go-to-market at Oracle and Salesforce implementing enterprise software for large organisations, and his core insight is that the interface itself has become the bottleneck, and what you'd do about it.

  • Most AI applications get ghosted. A day-five follow-up can double your response rate.

tracking Eragon

Applying to Eragon? Get the contact, not the form.

notify me when Eragon hires

The Eragon interview process

4 stages14 days typicaltake-home: yesmodelled from similar companies

We don't yet have verified candidate reports for Eragon. What follows is the typical process for a -person AI company, so treat it as a model, not confirmed detail.

Interview stages

  1. 1

    Recruiter Screen

    Phone or video Β· 30 min

    What it testsBasic qualification and logistics
    Usually run byRecruiter or HR
  2. 2

    Hiring Manager Interview

    Video call Β· 45 min

    What it testsRole fit and experience deep-dive
    Usually run byHiring manager
  3. 3

    Technical/Functional Round

    Video call Β· 60 min

    What it testsSkills assessment and problem-solving
    Usually run byTeam members
  4. 4

    Final Round

    In-person or video Β· 60 min

    What it testsCulture fit and cross-functional alignment
    Usually run bySenior leadership

Eragon take-home assignment

Eragon includes a take-home exercise in their interview process. For AI roles, this typically involves a practical problem that takes 2-4 hours. Focus on clean, working code over premature optimization. They're evaluating how you think and communicate, not just the solution.

Eragon interview timeline

Expect 14 days total. Compared to similar AI companies (14 days median), Eragon is about average.

Interviewed at Eragon?

Tell us how it went: stages, questions, timeline. Takes 90 seconds and it's how this page stays accurate for the next person.

Submit your Eragon interview experience β†’

Eragon jobs, frequently asked questions

How many jobs does Eragon have open?

4 open roles at Eragon, last checked October 2, 2026.

Does Eragon hire remotely?

Eragon doesn't have remote openings at the moment. All roles are in San Francisco.

What roles is Eragon hiring for?

Eragon is hiring across Engineering, Product, Design. The most recent opening is ML Engineer.

How do I apply for a job at Eragon?

Apply via the links above. For tips, read our guide on how to get hired at Eragon.

Does Eragon respond to cold emails?

Response rate data for Eragon not yet confirmed.

Who is the hiring manager at Eragon?

At this size, hiring is usually run by the hiring manager.

How competitive is it to get hired at Eragon?

Expect 100-250 applicants in the first two weeks for AI roles at this size. Apply within 72 hours for best odds.

How many rounds is the Eragon interview?

4 stages: Recruiter Screen, Hiring Manager Interview, Technical/Functional Round, Final Round.

Is the Eragon interview hard?

Focus on technical depth and system design, not puzzles. Technical Interview is reportedly the most challenging round.

Does Eragon give a take-home task?

Yes, Eragon includes a take-home assignment.

How long does Eragon take to get back to you?

Around 14 days across the full process.

What should I prepare for the Eragon interview?

technical depth and system design is the priority. Show you can work autonomously β€” that matters more than algorithms at people.

Where is Eragon based?

Eragon is headquartered in San Francisco.

tracking Eragonlast checked October 2, 2026

Get Eragon roles before they're posted

A role stays uncontested for about four days. Here's the window, and where we put you in it.

notify me when Eragon hires

From $5.99/week, cancel any time.

  1. livewatching Eragon0 applicants
  2. +2hrole spotted & verified1
  3. +4hyou get the alert1
  4. day 3you've applied~8
  5. day 7+hits the job boards250+

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