CTO / Quant Engineer

The Project

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

The Role

Quantitative background (MSc/PhD in computational finance, statistics, applied math, physics, or ML). Fluent in probabilistic programming (JAX, NumPyro, 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 HuggingFace. Thinks in distributions, not point estimates. Uncomfortable when a system returns a number without a credible interval.

What You Will Do

• 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 HuggingFace 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.
  

What We Are Looking For

Foundations

• Quantitative background (MSc/PhD in computational finance, statistics, applied math, physics, or ML)

• Experience with probabilistic programming and Bayesian inference (JAX, NumPyro, 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 (HuggingFace, Unsloth, PyTorch, etc.) and a knack for maximizing model performance on a startup budget.

Bonus Points

• Comfortable with agent-based modeling and economic simulation

• Familiarity with financial concepts (NAV, IRR, fund structures) — PE experience a plus but not required