---
title: 'Founding Software Engineer - Backend and Infrastructure at unsiloed.ai'
canonical: 'https://feeny.ai/job/founding-software-engineer-backend-and-infrastructure-unsiloed-ai-san-francisco-bj29hvewqt6m'
type: 'job'
last_seen: '2026-09-05'
---

# Founding Software Engineer - Backend and Infrastructure at unsiloed.ai

- **Company:** unsiloed.ai
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-12-31
- **Last confirmed live:** 2026-09-05
- **Apply:** https://jobs.gem.com/unsiloed-ai/am9icG9zdDqQgSFOr3DY5LAjjPQuxNbE

## Job description

We are hiring a Founding Software Engineer in San Francisco.

We are building a small, talent-dense team. This role will define the engineering archetype at Unsiloed AI and set the ceiling for the team. We strongly believe technical DNA compounds (or degrades) with every hire and hence the first few matter disproportionately. You will be expected to operate proactively, take full ownership, and independently drive systems from idea to production.

## What you Will Do

As a Founding Software Engineer, you will own the entire technical stack end-to-end, from core infrastructure and production systems to deployment, reliability, and developer experience.

- Architect, build, and scale core backend systems powering document intelligence and VLM-based workflows
- Own production infrastructure end-to-end: deployments, monitoring, performance, reliability
- Design and operate high-throughput, low-latency services in real production environments
- Take systems from R&D → production → enterprise scale
- Build and maintain cloud and on-prem deployments (Docker, Kubernetes, Helm) for enterprise customers
- Establish best practices for CI/CD, observability, debugging, and incident response
- Work closely with the founders and research team to turn research prototypes into production-grade, scalable systems.

## What We are Looking For

This role is backend & infrastructure-heavy. You should have most of the following:

- Experience building and operating scaled production systems
- Strong backend engineering skills (Python required; C++/Rust is a major plus)
- Experience with distributed systems (microservices, parallel processing, queues, caches like Redis)
- Deep familiarity with cloud infrastructure (AWS, GCP, Azure)
- Hands-on experience with Docker, Kubernetes, Helm, and infrastructure-as-code (Terraform / Pulumi)
- An ownership mindset
- Nice to have: Experience serving ML / VLM / GPU-heavy workloads

Compensation: $150k – $300k Equity: 0.1% – 1% Location: In-person, San Francisco Visa: Open to sponsoring

Hiring process: We don’t believe interviews alone can assess fit on either side. Our process centers around paid work trials, which can be done remotely. You will work with us on real problems, collaborate as peers, and get a genuine sense of what building Unsiloed AI feels like. For any questions, email hiring [at] unsiloed [dot] ai

## About unsiloed.ai

## Company Overview
- **One-liner**: Unsiloed AI provides a production-grade API that converts complex multimodal documents (PDFs, images, spreadsheets) into structured Markdown and JSON for LLMs and AI agents.
- **Entity Type**: Private (YC F25, Pre-seed round led by Y Combinator)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024 (per LinkedIn) / 2025 (per Y Combinator profile) – conflicting reports
- **Founders**: Aman Mishra (Co-Founder & CEO) and Adnan Abbas (Co-Founder & CTO)

## Core Business
- **Primary industry**: Document AI / Unstructured data infrastructure for LLMs and AI agents
- **Target customers**: B2B – Enterprise (Fortune 150 banks, NASDAQ-listed companies) and startups in accuracy‑sensitive verticals (finance, legal, healthcare)
- **Mission or purpose**: “Build the infrastructure layer for turning unstructured data into structured, queryable, and LLM‑ready assets.”

## Products & Services
- **[Unsiloed AI API](https://www.unsiloed.ai/)**: A set of three core capabilities (Parse, Extract, Split) that can be used standalone or chained end‑to‑end. The API ingests PDFs, images, spreadsheets, and scanned documents, and outputs clean Markdown and schema‑validated JSON with confidence scores per field. It handles mixed layouts (tables, charts, forms, handwriting, multi‑page tables) and preserves document hierarchy.

## Market Standing
- **Valuation**: Not publicly available
- **Key Metric**: Total Funding – Pre‑seed round (announced 2025-10-08) with 3 investors, led by Y Combinator (primary partner: Nicolas Dessaigne)
- **Notable Investors/Partners**: Y Combinator (backed through Batch F25)
- **Growth Signals**: Already processing “millions of pages of complex documents each week” for Fortune 150 banks, NASDAQ‑listed enterprises, and 10+ YC startups. Public benchmarks claim #1 accuracy vs. LlamaIndex, Gemini, Mistral, and Unstructured.io. Team size: 2 employees (–33.3% YoY according to LinkedIn).

## Competitive Advantages
- **Proprietary dual‑stream vision model**: A data stream captures tokens/numbers/entities while a layout stream captures image tokens, bounding boxes, alignment, and indentation hierarchy; a cross‑attention layer fuses both for reasoning over content *and* structure.
- **Domain‑specific decoder**: Trained on millions of real enterprise documents (not synthetic data) across legal, finance, healthcare; outputs are schema‑conditioned with cross‑field constraints.
- **Heatmap‑based chunking**: Focuses compute on pivot zones (numerical columns, merged cells), preserving related information across page breaks.
- **Air‑gapped deployment**: Supports managed and air‑gapped on‑prem environments for privacy‑sensitive verticals, with identical API surface.

## Strategic Focus
- **Current priorities**: Scale the API to serve “more AI agents reading from and writing to documents than humans.” Continue improving domain‑specific decoder accuracy via reinforcement learning pipeline and expand enterprise sales, especially in finance, legal, and healthcare.
- **Direction for growth**: Build the universal interface layer for unstructured data in AI workflows.

## Why Work Here
- **Culture highlights**: Small team of ex‑Quant developers and AI researchers; YC‑backed startup with direct exposure to founders and high‑impact work.
- **Remote/hybrid/office policy**: In‑office at San Francisco (all open roles listed as “In office” on [Gem careers](https://jobs.gem.com/unsiloed-ai)).
- **Notable perks or engineering culture**: Opportunity to define the infrastructure layer for AI agents, work on cutting‑edge vision models, and ship a product already used by Fortune 150 enterprises. Open roles: Founding GTM Lead, Founding ML Researcher, Founding Software Engineer (Backend and Infrastructure).

## Sources
1. [unsiloed.ai](https://www.unsiloed.ai/) – Official product page
2. [unsiloed.ai/careers](https://www.unsiloed.ai/careers) – FAQ, technical details
3. [ycombinator.com](https://www.ycombinator.com/companies/unsiloed-ai) – YC company profile (founding, team, funding)
4. [linkedin.com](https://linkedin.com/company/unsiloed-ai) – LinkedIn company page (headcount, financials, founders)
5. [jobs.gem.com](https://jobs.gem.com/unsiloed-ai) – Current open positions and work location

## Other roles at unsiloed.ai

- [Founding GTM Lead](https://feeny.ai/job/founding-gtm-lead-unsiloed-ai-san-francisco-het4pd8chmge) — San Francisco, CA
- [Founding ML Researcher](https://feeny.ai/job/founding-ml-researcher-unsiloed-ai-san-francisco-jj09z4hhjseb) — San Francisco, CA