Role overview
I’m working with a top-tier systematic trading group building out a new AI/ML initiative focused on LLM agents and internal automation. This is one of the few roles in industry where you get to apply frontier agentic workflows
directly
inside a high-performance research environment that actually ships to production, not demos.
They’re hiring an
ML Engineer
to:
- Design and implement LLM-based agents that autonomously execute multi-step workflows for quant researchers & developers
- Build and deploy RAG pipelines pulling context from both structured + unstructured internal data
- Partner closely with QDs/QRs to identify high-ROI automation opportunities across research and data engineering
- Contribute to internal ML frameworks, testing, CI/CD, and ML-driven research tooling
- Own end-to-end Python/ML systems using vector stores, retrieval frameworks, and orchestration stacks (LangChain, LlamaIndex, etc.)
Why this is compelling:
- Work on applied agentic AI where everything you build gets used immediately by researchers
- Join a small, high-impact team with direct visibility across the organization
- Access to real data, real infrastructure, and real problems — not synthetic benchmarks
- Strong compensation with upside, plus long-term career growth inside a world-class trading environment
Total comp: 250k base, up to 800k TC
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Ai Machine Learning Data Engineer Robotics Generative Ai