Sphinx

Cyber Staff • San Francisco, CA

1d • $150,000 - $280,000

About The Position

As a backend engineer, you will build and maintain the backend and ML systems powering Client's AI agents. These systems navigate the web, interpret unstructured data, detect global financial risk, and make sub-second compliance decisions. The role covers backend engineering, distributed systems, ML pipelines, and agent workflows. You will own features end-to-end and ship production systems used by banks. This Role Is Ideal For Engineers Seeking High technical scope Challenging problems with real-world impact Minimal meetings A small team with high autonomy Opportunities to push the frontier of AI agents in production environments Key Technical Challenges Browser Agents for the Invisible Web Build AI agents that interact with legacy government portals and financial systems using: Computer vision DOM reasoning Robust error handling at scale Global Risk Graph Develop a unified intelligence layer connecting people, companies, and risk signals across jurisdictions and languages. Decisions at the Speed of Money Build infrastructure that processes millions of transactions on AWS, including: Distributed inference Caching Queue orchestration Self-healing data pipelines Deep Research Without Hallucinations Develop deep research pipelines to ensure LLMs do not confuse similar entities, providing accurate compliance decisions beyond the capabilities of generic models like ChatGPT.

Requirements

• 2–8+ years of software engineering experience. (preferred 4-8yrs)
• Strong backend engineering background (Python preferred).
• Experience with AWS , distributed systems, or ML pipelines.
• Proven track record of shipping production systems.
• Strong communication skills and ability to operate with minimal process.
• Comfortable interacting with clients when needed.

Responsibilities

• Architect and ship backend systems used by AI agents.
• Build ML/agent pipelines, distributed inference, and automation frameworks.
• Own features vertically: design -> build -> test -> deploy -> iterate.
• Handle large-scale data, global risk signals, and compliance edge cases.
• Experiment with frontier AI models and agentic architectures.
• Collaborate directly with founders, researchers, and customers.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed