CTO / Quant Engineer

enzianlabs; Zürich; Vor Ort; CHF 140.000 - 200.000; Vollzeit

Zusammenfassung

enzianlabs is building an AI backbone for private equity, seeking a quantitative scientist to design and deploy end-to-end ML systems. You will work on data pipelines, probabilistic programming, and large-model fine-tuning for finance tasks.

You will handle messy inputs like scanned PDFs, apply Bayesian methods to quantify uncertainty, and optimize models with modern tools like JAX, PyTorch, HuggingFace, and Unsloth. Zurich-based, on-site role with a startup mindset.

Qualifikationen

• Quantitative track with MSc/PhD in a relevant field.
• Hands-on experience with probabilistic programming and Bayesian inference.
• Experience building production-grade ML pipelines on messy data.
• Fine-tuned LLMs and vision models for domain tasks.
• Familiarity with modern AI stack and financial domain concepts is a plus.

Aufgaben

• Own end-to-end ML lifecycle from notebooks to prod systems.
• Build data ingestion pipelines for noisy real-world inputs.
• Fine-tune LLMs and vision models for domain tasks.
• Apply LoRA, quantization and efficient training for cost efficiency.
• Model uncertainty with Bayesian methods for valuations.

Kenntnisse

Quantitative PhD

Probabilistic programming

Bayesian inference

LLM fine-tuning

Production pipelines

Data ingestion

Ausbildung

MSc/PhD in computational finance / statistics / applied math / physics / ML

Tools

JAX

NumPyro

PyMC

HuggingFace

Unsloth

PyTorch

Jobbeschreibung

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