Quantitative Researcher, Agentic & LLM-Driven Systematic Equities

The Mandate A quant fund built specifically around agentic and LLM-driven research methods is hiring a quantitative researcher to run short-horizon systematic equities research on top of that infrastructure. Signal candidates sourced from filings, transcripts and news flow through agentic pipelines are core to how the fund generates and validates ideas, not an experimental side project. This is an alpha research seat, not a book-ownership seat. Sizing and deployment decisions sit with portfolio construction; the mandate here is signal generation and validation at the pace the pipeline can produce candidates. The Hard Questions (What You Will Solve) Validating Machine-Generated Ideas: an agentic pipeline can generate equity signal candidates from earnings transcripts and filings faster than any research team could manually. What is your process for distinguishing a genuine information edge from an artefact of the pipeline's own search process, before it touches live risk? Short-Horizon Decay in a Crowded Space: NLP-driven equity signals from public filings and transcripts are now targeted by a wide range of funds. How do you extract short-horizon edge from text-derived signals once the underlying data source is no longer a differentiator? Sizing a Higher Throughput of Candidates: the fund's edge is partly in how fast the pipeline generates and validates new signal candidates. How do you build a portfolio construction framework that absorbs a higher rate of candidate signals without diluting returns through overfitting? The Structural Edge AI-Native Research Environment: agentic pipelines, context engineering and output validation are core infrastructure, not an experimental layer bolted onto a legacy stack. Direct Strategy-to-Capital Pipeline: validated signals move to live risk quickly, with attribution clear at the strategy level. Ideal Profile The Metric: 5+ years as a quantitative researcher in systematic equities, with genuine comfort operating alongside AI-assisted research methods rather than using them purely as a coding aid. The Tech: Python at production level, with demonstrated experience operating or building agentic research systems for signal discovery from unstructured text (transcripts, filings, news), including context engineering and output validation. Compensation \& Preferences Non-compete: Preference for candidates with immediate or near-term availability. Compensation: £150,000 to £185,000 base plus a performance-based payout tied to attributed strategy P\&L. This is not a guarantee of compensation or salary; a final offer amount may vary based on factors including but not limited to experience, domain expertise, and geographic location. Apply Now At Onyx Alpha Partners, we are committed to connecting the most sought after talent in the financial world, to opportunities that expand the universe of unconstrained performance within their chosen discipline. If this opportunity aligns with your career aspirations, we encourage you to apply and explore the potential for growth and unparalleled success.