Infrastructure Engineer, Applied AI
Company websiteparadigm.xyz (Paradigm, opens in new tab)
Base salary $250K to $400K
Employer-published annual base salary, USD. Advertised base range in San Francisco, CA
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSan Francisco, CA
- WorkplaceOn-site
- Status at last checkSource accepting applications when checked
- Last checked (3 h ago) · observed since
About this job
INFRASTRUCTURE ENGINEER, APPLIED AI
The Firm
Paradigm is a San Francisco-based investment firm focused on frontier technologies across the globe, with over $11 billion in assets under management. We make investments in companies and protocols at all stages, ranging from early-stage venture financing rounds to growth equity to liquid assets.
Paradigm was co-founded in 2018 and is co-led by Matt Huang and Alana Palmedo. We’ve been hard at work investigating the world’s most beautiful technical problems since day one. Our research-driven approach helps us build relationships with founders and entrepreneurs, but it also reflects our broader goal of accelerating the progress of frontier technology.
The Role
Over the last eight years Paradigm has built some of the most used open-source software in the crypto industry, including Reth and Foundry. Earlier this year we published EVMBench with OpenAI, launched Optimization Arena and hosted the Auto Research hackathon. More recently, we open-sourced Centaur, a self-hosted runtime for multiplayer AI agents, which we’ve been using to transform how we work at Paradigm and Tempo.
All of these were done part-time by people on Paradigm’s Investing & Research team. This has made us realize that we are people-bound, not idea or impact bound. We need to grow our team to achieve our ambitions.
We want to do three things:
Experiment aggressively. We will push the capabilities of models, harnesses and infrastructure to the limit in service of Paradigm’s broader goals in investing, research, and building.
Build in public. We will get our hands dirty building useful open-source products.
Invest. We will leverage our infrastructure - an area we think is still ripe for disruption with AI - to be better investors. By understanding infrastructure gaps experientially, we may incubate companies that will accrue value by addressing these gaps.
As we deploy more long-running agentic capabilities, we may get into fundamental research, forecasting, model cost optimizations etc., but that is not our focus yet.
We are looking for fearless engineers for this project who understand infrastructure, security, AI models, harnesses, and Slackbots. You will work directly with Georgios, Arjun, Matt Slipper, and the broader Paradigm team, alongside Tempo, and other companies that are themselves operating at the frontier.
Your work will directly impact how Paradigm operates every day. If this sounds like you, reach out.
Responsibilities
Control plane and durability: Own the core services that coordinate agent runs, keeping execution state durable and recoverable through restarts, failovers, and everything that goes wrong in production.
Execution runtime: Build and harden the sandboxed, Kubernetes-based runtime that isolates agent sessions, covering provisioning, lifecycle, and the network controls that make untrusted agent code safe to run.
Secrets and secure egress: Maintain the credential model that lets agents call third-party APIs without ever holding raw keys.
Reliability and observability: Own monitoring, alerting, incident response, and the failure-recovery behavior the system relies on.
Self-hosted deployment: Keep Centaur something any organization can run inside its own walls, with clean deployment, sane configuration, and painless upgrades.
Qualifications and Experience
A track record of building world-class products
Experience applying modern AI and software systems to solve complex, ambiguous problems end-to-end
Comfortable navigating everything from foundational models to full-stack product delivery
Pragmatic, product-minded builder who cares about solving real problems rather than overfitting to academic ML goals
Ability to work autonomously and as a collaborator on the team
Attributes
Exceptional team player
Extreme open-mindedness
Clarity of thought
Clear and concise communication (both written and verbal)
Technical depth; analytical; rigorous
Highly curious; fast learner
Ability to bridge technical and investing mindsets
Interest in frontier technologies and crypto markets
Source: Paradigm on Ashby · View original posting (opens in new tab)
Pay in context
This role's base floor ($250,000) is above 94% of the 10,389 Software Engineering roles nationwide listed on this site; 69 others list the same floor. Their median floor is $160,000; the middle half runs from $140,000 to $191,000.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 7, 2026.
This listingInfrastructure Engineer, Applied AI$250K to $400K
Product Engineer, Applied AI$250K to $400K
Controller$250K to $350K
Executive Assistant$120K to $160K
Chart as a table
| Role | Published base |
|---|---|
| Infrastructure Engineer, Applied AI (this listing) | $250K - $400K |
| Product Engineer, Applied AI | $250K - $400K |
| Controller | $250K - $350K |
| Executive Assistant | $120K - $160K |
Observed changes
- Began meeting the published-salary criteria
- First observed on the employer's board
- fields changed
- Observed since
- Employer-stated posting date
- 2026-08-03
Where this pay comes from
Location as the employer wrote it: San Francisco, CA
USD 250,000 – 400,000 · 1 YEAR · Salary
- Method
- Structured pay field published by the employer
- Checked
- Rule set
- us-base-v2
The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
