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Sphinx

AI-driven compliance automation for financial institutions

Forward Deployed Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • Solve problems end-to-end by architecting agent behaviors, building backend systems, optimizing machine-learning pipelines, and shipping product features.
  • Build high-quality systems for uptime and reliability, accounting for failure points in financial-crime workflows.
  • Work effectively in ambiguous, client-specific environments and become fluent with the product.
  • Translate client needs into precise technical requirements and explain technical issues clearly.
  • Be motivated by the impact of preventing money laundering and terrorism financing.
  • Prefer building, experimenting, and fixing problems directly rather than relying on bureaucracy.

Responsibilities

  • Design flexible integration layers, map complex workflows, and make agents resilient to interface changes and client-specific requirements.
  • Ship fixes in live client environments, build diagnostic tools, and feed deployment learnings back into the product.
  • Work directly with compliance officers and analysts to translate standard operating procedures into structured agent logic.
  • Lead deployments through their full lifecycle, from the first client call to production rollout, bridging engineering, product, and client success.
  • Build agents that navigate legacy systems, government portals, and third-party platforms using browser interaction, computer vision, DOM understanding, and robust error handling.
  • Support risk-signal processing at high volume and low latency on AWS through distributed inference, caching, queue orchestration, and self-healing data pipelines.

About the Company

Sphinx

AI-driven compliance automation for financial institutions

Sphinx provides AI-powered compliance software for financial institutions to automate KYB, KYC, and AML tasks. Its browser-native AI agents run inside a client’s existing tools (like case management systems and dashboards) to imitate human actions such as due diligence, alert handling, document verification, and transaction monitoring, learning the company’s procedures so it can start by suggesting decisions before moving toward end-to-end automation. This agent-based approach requires little engineering from the client and aims to reduce manual reviews and backlogs while lowering operating costs, with examples like Equals Money reporting a significant drop in false positives and improved true positives. The company targets banks, fintechs, credit unions, and crypto platforms worldwide, and plans to expand its deployment and enter new European and Asian markets, funded by seed investors to accelerate growth.

Company size
11-50
Company stage
Seed
Total funding
$7.2M
Headquarters
San Francisco, CA
Founded
2024

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