Sphinx Labs — Member of Technical Staff

Type: Full-time | On-site | San Francisco, CA Compensation: $120,000–$300,000 + Competitive Equity Hiring count: 3 Visa sponsorship: Yes — H-1B, O-1, OPT Reports to: Not specified

Sphinx builds AI compliance analysts that work inside browsers, APIs, and internal systems just like human analysts — automating AML, KYC, KYB, and transaction monitoring end-to-end for banks and fintechs. The pitch is that compliance is a large, broken, underserved market and Sphinx is building the intelligence layer that protects the financial system.

Founded: 2024 | Team size: 1–10 | Stage: Seed | Backing: YC-backed, $7M+ seed raised Industry: FinTech Website: sphinxhq.com Office: San Francisco, CA Traction: Deployed with banks and fintechs across the U.S., Canada, Europe, and LatAm; reports ~90% less manual work and ~4× lower costs for customers; claims to have won every competitive RFP to date.

• Frontier AI agents in production: Build browser-based agents for the "invisible web" — computer vision, DOM understanding, robust error handling at scale, and novel agentic workflows with no Stack Overflow answers.
• Massive, unsexy market: Banks spend ~$50B/year on manual compliance analysts; the space has seen little innovation and customers are frustrated with well-funded incumbents.
• Strong traction and backing: YC-backed, $7M+ seed; live across the U.S., Canada, Europe, and LatAm with measurable customer wins.
• Vertical ownership on a small team: Own features end-to-end — agent behaviors, backend systems, ML pipelines, and product — often in the same week.

• No intake call transcript is available on the role page — only an intake video (not transcribed here). Update once a summary is provided.

A backend-leaning Member of Technical Staff building the end-to-end systems that power Sphinx's production AI compliance workflows — high-performance infrastructure, reliability, and backend architecture supporting mission-critical automation.

• →Architect agent behaviors and build the backend systems behind production AI compliance workflows
• →Build browser agents that navigate legacy systems, government portals, and third-party platforms (clicking, scrolling, interpreting unstructured data)
• →Process risk signals at the speed money moves — millions of transactions, sub-second decisions — via distributed inference, caching, queue orchestration, and self-healing data pipelines on AWS
• →Contribute to an in-house deep-research pipeline that keeps LLMs from conflating similar entities
• →Optimize ML pipelines and ship product features, owning problems vertically

Tech stack: Python, AWS (distributed inference, caching, queue orchestration, self-healing pipelines); browser automation, computer vision, LLM systems.

• Experience with backend development and/or ML, especially in Python and AWS
• Hands-on experience deploying software to production environments
• Strong communication skills and willingness to interact with clients

• Experience with browser automation and computer vision
• Background with distributed inference and optimization on AWS
• Familiarity with LLM systems and deep research pipelines
• Experience building systems that process millions of transactions at scale
• Knowledge of compliance, AML, KYC, or financial crime domains
• Competitive coding background
• Contributions to open-source projects or personal projects on GitHub
• Prior experience as a founder or at an early-stage startup

• Candidates from a pure data science background who lack strong technical skills
• Lack of hands-on experience deploying software to production
• Primarily academic or research experience without practical engineering work

Salary$120,000–$300,000EquityCompetitive Equity On-site policy On-site, San Francisco Visa sponsorshipH-1B, O-1, OPTExperience1+ years Employment type Full-time Location San Francisco, CA

1. Are you based in the Bay Area or willing to re-locate?
2. GitHub Profile
3. LinkedIn Profile
4. Attach a screenshot of a coding agent session you're particularly proud of (optional)

No separate call-only Screening Questions were provided on the role page.

Stage 1 — Intro Call — Fit & motivation; a small amount of whiteboarding on the call. Stage 2 — Technical Screening / Take-Home Stage 3 — Deep-Dive Technical Interview — No live coding. Stage 4 — Paid In-Person Trial — Short. Stage 5 — Offer Extended Stage 6 — Candidate Hired — Candidate accepts and starts.