Unsiloed AI — Founding Backend Engineer

Type: Full-time | On-site | San Francisco, CA Compensation: $150,000–$300,000 + 0.1%–1% equity Hiring count: 1 Visa sponsorship: Yes — H-1B, O-1, OPT Reports to: Founders / research team (not explicitly stated; Initial Chat is with Aman)

About Unsiloed AI

Unsiloed converts complex documents into LLM-ready data. Production-grade APIs ingest, parse, structure, and split documents across formats like PDF, PPT, and Excel into clean Markdown and JSON that AI agents and LLMs can reliably work with from day one — preserving document structure and hierarchy while capturing domain-specific context for high-stakes workflows across Finance, Legal, and Healthcare.

Founded: 2024 | Team size: 1–10 (Seed) | Total funding: $7.5M (Seed) Industry: AI Tools Website: unsiloed.ai Office: San Francisco, CA

Why Candidates Should Join

• Founding technical seat: This role defines the engineering archetype and sets the technical ceiling for the team — the first few hires matter disproportionately.
• End-to-end ownership: Own the full stack from core backend architecture and infrastructure through deployment, reliability, and developer experience.
• Real enterprise traction: Already deploying production AI for enterprise customers across high-stakes verticals (Finance, Legal, Healthcare).

Intake Call Summary

• No intake transcript was included in the role page (an intake video is present but not transcribed). Request a summary from Contrario if intake detail is needed before sourcing.

The Role

Founding Software Engineer based in San Francisco, building toward a small, talent-dense team. The role is backend & infrastructure-heavy: own production systems end-to-end and turn research prototypes into production-grade, scalable systems alongside founders and the research team.

What You'll Be Doing

• 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 they're looking for (from role body — skills profile for sourcing)

• 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

Tech stack: Python (required); C++/Rust (plus); distributed systems (microservices, queues, Redis); cloud (AWS/GCP/Azure); Docker, Kubernetes, Helm; Terraform / Pulumi

Requirements

• 2-5 years of experience
• Ability to work in-person at San Francisco office
• Hands-on experience building backend infrastructure

Green Flags

• Graduated from top-tier schools (IIT Bombay, Stanford, etc.)
• Very early stage startup experience (first 1-3 engineers)
• Experience at companies with strong technical DNA
• Research experience

Red Flags

• Only big tech experience (Google, Microsoft, Amazon) without startup exposure
• Experience only on abstract/high-level work without hands-on technical involvement
• 10+ years experience (too senior, not hands-on enough)
• Only enterprise/large company experience without touching core technical elements

Role Details

Salary$150,000–$300,000Equity0.1%–1%On-site policyIn-person, San Francisco officeVisa sponsorshipH-1B, O-1, OPTEmployment typeFull-timeLocationSan Francisco, CA

Benefits & Perks: Competitive compensation + equity · Full health insurance coverage · Free company laptop · Doordash/Uber credits

Screening Questions

• None provided on the role page. Confirm with David whether Contrario or the client supplied screening questions separately.

Interview Process

Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Initial Chat — Initial conversation with Aman to discuss background and fit. Stage 3 — Technical Coding Round (1 hour) — Claude Code/Codex-assisted round solving a small, hard problem. Not DSA/Leetcode. Stage 4 — Work Trial — One-week flexible work trial (can be done remotely, on weekends or evenings depending on candidate schedule). Stage 5 — Offer ExtendedStage 6 — Candidate Hired — Candidate accepts and starts.

Ideal Companies & Backgrounds

From the role page (8 companies; appeared fully expanded): Databricks, Glean, Rubrik, Snorkel, Google DeepMind, Parallel Web Systems, WisdomAI, Cohere

Ideal Candidate Profiles

For reference only — DO NOT CONTACT. (LinkedIn links were not captured in the paste — only the LinkedIn buttons; re-grab URLs if needed.)

• Aman Kansal
• Tushar Gupta
• Kapil Vaidya
• Edward He

Rejected Candidate Feedback

• None provided on the role page.