Member of Technical Staff

Sphinx Labs — Member of Technical Staff (Backend) 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 About Sphinx Labs 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 raisedIndustry: FinTechWebsite: sphinxhq.comOffice: San Francisco, CATraction: 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. Why Candidates Should Join 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. Intake Call Summary No intake call transcript is available on the role page — only an intake video (not transcribed here). Update once a summary is provided. The Role 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. What You'll Be Doing 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. Requirements 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 Green Flags 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 Red Flags Candidates from a pure data science background who lack strong technical skills Lack of hands‑on experience deploying software to production Role Details 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 Required Candidate Q&A (Contrario submission form) Are you based in the Bay Area or willing to re‑locate? GitHub Profile LinkedIn Profile 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. Interview Process 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. #J-18808-Ljbffr