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.

Foundations

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

Experience

• 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