Head of Applied AI – Trading Systems

Artificial IntelligenceArtificial IntelligenceFull TimeRemoteTeam 11-50Since 2025Company SiteLinkedIn

Location

United States

Posted

15 days ago

Salary

Not specified

EnglishAirflowCloudNumpyPandasPython

Job Description

• You will design and maintain automated pipelines that ingest, clean, and normalize: • News, filings, earnings calls, macro releases • Social and sentiment data • Alternative and proprietary datasets • Your job is to replace manual refresh workflows with push-based alerts that surface only what matters — mapped directly to: • Watchlists • Live positions • Risk exposure • Reliability matters. Latency matters. Silence matters. • You will deploy LLM-powered systems to summarize, extract, compare, and reason over: • 10-Ks, 10-Qs, earnings calls, central bank minutes • Sell-side research and internal notes • You will build “chat with our data” tools that allow traders to query proprietary research in natural language. • You will also create tools for fast discretionary back-testing: • How did this asset behave during the last three macro shocks of this type? • You will build the filters that decide what breaks through. • Sentiment and relevance scoring • Entity recognition that maps events to exposure • Dashboards that surface regimes, anomalies, and dislocations — not vanity metrics • If something matters, it should scream. If it doesn’t, it should disappear. • You own the compute layer that runs the intelligence system. • Cloud and/or local GPU infrastructure • Vector databases and retrieval systems • Data-privacy-first architectures (local models where required) • You choose the architecture. You ship what runs fastest and breaks least. Our proprietary data and strategies do not leak. Ever.

Job Requirements

  • Built your own projects
  • Traded your own account
  • Worked in a high-stakes startup, prop shop, or family office
  • You hate waiting for permission
  • Python (Pandas, NumPy) is non-negotiable
  • You have hands-on experience with:
  • LLMs and RAG architectures
  • Prompting, evaluation, and failure modes
  • Vector databases (Pinecone, Milvus, FAISS, etc.)
  • Orchestration (LangChain, Airflow, or custom agents)
  • REST & WebSocket APIs (market data, news, internal tools)
  • Lightweight internal UIs (Streamlit, Dash, Retool, etc.)
  • You know the difference between an LLM demo and a production system
  • You care about latency, hallucinations, and context windows
  • Financial data is messy, adversarial, and time-sensitive
  • Narratives are not facts
  • A 10-K is not a blog post
  • You are comfortable making judgment calls under uncertainty.

Benefits

  • Zero Latency
  • Real Impact
  • Sovereignty

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