CTO​/Quant Engineer

The Project

We are building an AI system that is the backbone for the private equity industry.

Quantitative background (MSc/PhD in computational finance, statistics, applied math, physics, or ML). Fluent in probabilistic programming (JAX, Num Pyro, PyMC). Hands‑on experience building data pipelines on real‑world messy inputs. Has fine‑tuned large models for domain‑specific tasks using frameworks like Unsloth or Hugging Face. Thinks in distributions, not point estimates. Uncomfortable when a system returns a number without a credible interval.

• Build Production Pipelines:
  Take ownership of the end‑to‑end ML lifecycle. You will transition models from local Jupyter notebooks to scalable, production‑ready systems.
• Tame Messy Data:
  Architect data ingestion pipelines capable of handling noisy, real‑world inputs—including scanned PDFs, inconsistent reporting formats, and missing data points.
• Leverage Foundational Models:
  Fine‑tune LLMs and vision models for domain‑specific financial tasks.
• Optimize for Efficiency:
  Apply techniques like LoRA, quantization, and efficient training loops using frameworks like Unsloth and Hugging Face to make large‑scale AI practical and cost‑effective.
• Apply Advanced Mathematics:
  Utilize Bayesian inference and probabilistic programming to model uncertainty in private market valuations.

• Quantitative background (MSc/PhD in computational finance, statistics, applied math, physics, or ML)
• Experience with probabilistic programming and Bayesian inference (JAX, Num Pyro, PyMC)

• Engineering Chops:
  Proven experience building production pipelines. You know firsthand the critical difference between a proof‑of‑concept demo and a resilient production system.
• Applied AI/GenAI:
  Hands‑on experience working with foundational models. You have successfully fine‑tuned LLMs.
• Resourceful Tooling:
  Deep familiarity with the modern AI stack (Hugging Face, Unsloth, PyTorch, etc.) and a knack for maximizing model performance on a startup budget.

• Comfortable with agent‑based modeling and economic simulation
• Familiarity with financial concepts (NAV, IRR, fund structures) — PE experience a plus but not required