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Sixtyfour (YC P25) Is Hiring

Original article
ycombinator.com|1 points|0 comments|by HPMOR|Aug 20, 2026

🚀 Sixtyfour (YC P25) Is Hiring

Sixtyfour Logo

💼 Role Overview: Software Engineering Intern

Sixtyfour is building the intelligence infrastructure for people and entities. We are looking for a high-caliber intern to join our engineering team in San Francisco.

DetailSpecification
Compensation\text{\6,000} - \text{$10,000}$ / month
LocationSan Francisco, CA, US (In-person)
Role TypeInternship →\rightarrow→ Engineering (Backend)
EligibilityJunior year of university and above
Visa Support✓\checkmark✓ Will sponsor
Core StackPython, TypeScript, AWS, Kubernetes, Docker
AI FocusMachine Learning, Fine-tuning, Evals

🎯 The Opportunity

Sixtyfour transforms a basic identifier—like a domain, email, or name—into a comprehensive, verified profile of a company or individual. We achieve this by deploying AI agents that conduct open-web research with the precision of a professional analyst, subsequently verifying and scoring the gathered intelligence.

As an intern, you won't be doing "busy work." You will build the reasoning agents, the scalable infrastructure that powers them, and the user-facing products that deliver the insights.

🛠 Our Engineering Philosophy

We believe in thinking before typing. We dedicate roughly 80% of our cycle to deep problem analysis, architectural planning, and system design.

If you enjoy arguing over failure modes, weighing tradeoffs, and designing for scale on a whiteboard, you will thrive here.


📝 What You'll Do

  • Agent Development: Create AI agents for OSINT and deep-web research. You'll design systems that scour public records, social media, and the open web to structure complex entity data.
  • Architectural Ownership: Focus on the thinking phase. You are expected to weigh designs and write detailed plans before writing a single line of code.
  • Scalability Engineering: Proactively solve for latency+cost+concurrency\text{latency} + \text{cost} + \text{concurrency}latency+cost+concurrency to ensure systems survive massive data volumes.
  • End-to-End Delivery: Own the full lifecycle—design →\rightarrow→ build →\rightarrow→ test →\rightarrow→ deploy—shipping features in weeks, not quarters.
  • Accuracy Optimization: Refine agents to ensure enrichment returns high-fidelity, well-sourced answers.
  • Engine Expansion: Integrate new tools and data sources into the enrichment engine.
  • Evidence-Based Improvement: Develop rigorous evals and tests to prove that model changes actually improve results (no "hope-driven" development)\text{(no "hope-driven" development)}(no "hope-driven" development).
  • Reliability: Optimize long-running jobs via caching, batching, and orchestration to handle millions of records.

🔍 Who We Are Looking For

🔴 The Must-Haves (High Bar)

  • Deep Fundamentals: A strong grasp of how the web, APIs, databases, and concurrency work under the hood.
  • First-Principles Thinking: The ability to take a vague problem, decompose it, and design a robust solution.
  • Scale Mindset: You naturally consider data volume and cost. You can provide concrete examples of systems you've built or scaled.
  • Proven Execution: You have shipped a real project (Open Source, Research, Hackathon) and can defend every design decision you made.
  • Polyglot Ability: Proficiency in at least one language and the ability to learn others rapidly.
    • Our Stack: Python (AI/Backend) and TypeScript/React (Product).
  • Ownership: A fast-moving, autonomous worker capable of working in-person in SF.

🟢 The Nice-to-Haves (Bonus)

  • OSINT Expertise: Experience in identity resolution, reverse username lookups, or entity mapping.
  • Agentic AI: Experience building RAG apps, agent loops, or complex scrapers using LLMs.
  • Distributed Systems: Familiarity with workflow engines, queues, or high-throughput pipelines.
  • Frontend Skills: Experience with React or Next.js for data-heavy interfaces.
  • Production Ops: Experience managing Postgres, Redis, or observability tools in production.
  • Data Intuition: The ability to spot when an AI answer is "subtly wrong."

Note: If you have a non-traditional background or lack a specific tool, don't hesitate apply anyway.


🎁 Why Join Us?

  • Real Impact: You will own features that land in the hands of paying customers.
  • Elite Mentorship: Work directly with engineers solving hard applied AI problems (research agents and large-scale evals).
  • Architectural Growth: You'll leave as a stronger engineer—better at judgment and architecture, not just faster at typing.
  • Unlimited Resources: We provide a real budget for tooling and LLM usage; we don't ration tokens.
  • Founder Mindset: You'll be encouraged to scope your own work and make the final call.
  • Perks:
    • Office lunches 🍱
    • Team offsites ✈️
    • Open to Juniors, New Grads, and Early-Career Engineers.

📩 Application & Process

How to apply: Show us something you built. Be prepared to go deep on the why—the designs you rejected and how the final version handles scale.

The Interview Pipeline:

  1. Screening (15 min)
  2. Take-home assessment
  3. Virtual onsite (1 hr)
  4. Founder Call

🏢 About Sixtyfour

We create AI research agents capable of discovering, linking, and reasoning across all available data regarding people and companies, turning that intelligence into automated workflows for marketing, recruiting, and sales.

Company Snapshot:

AttributeDetail
Founded2025
YC BatchP25
Team Size12
LocationSan Francisco
FoundersChristopher Price & Saarth Shah

The Leadership Team: ![Saarth Shah](https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ae314900a25755eb76714796bd4247b017213d5b.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECAFC7QV3JU%2F20260820%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260820T195142Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLXdlc3QtMiJIMEYCIQD8tlNCWGdL5e6yZ54tFRDMl4a%2F23z1agHSOSaECeuWqQIhAKjgYfsuzFkEfqrM8NjM2T5LkvyoA8E4uJmAwRkjBNSPKu4DCJX%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQABoMMDA2MjAxODExMDcyIgxznR2vk0uYZEm0ZYkqwgPuzixpbP2T3e%2FiabSek2SG%2FzL%2BV5SW3XdSlwhIDZwQ0eI2%2BJcskV22u2ACSwSugpo1o92lIBalUJrRWX9l2pbxIIw1fRh6cvbfBXWWDTmqSUkUUZX01zDtCtdcsHNBv%2Bj3t53fShKWwtT90YlmSQWqtdfkjQ5P5qNIXkdHuVkWWpUs%2FhaljsBSvTPYs4jHqDRZ1SXj7HQ4CuYii8PHLa%2BI5Afn90PrcslRGPhimlEYFCfzoUZ1pwss9cTrM8ZpVQQ%2BMArQKH%2B9U5%2Bz8fcMML323GpdjHjA0PVvIo0NxWaRt1cBnY5uN6UzG3sDpdScmNmkMTrOWxkZy7