CTO / Quant Engineer
On-siteZürich, Zurich, Switzerland
Job Summary
Build production ML pipelines that transition models from local notebooks to scalable systems handling noisy inputs like scanned PDFs and inconsistent reporting formats. Fine-tune large language and vision models for domain-specific financial tasks using frameworks such as Unsloth and HuggingFace, applying LoRA and quantization to optimize efficiency. Apply Bayesian inference and probabilistic programming to model uncertainty in private market valuations, ensuring systems output credible intervals rather than point estimates. This role requires an MSc or PhD in computational finance or applied math with strong engineering chops to manage the end-to-end lifecycle of AI systems powering the private equity industry backbone.
Required Qualifications
- 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
- Proven experience building production pipelines
- Experience with probabilistic programming and Bayesian inference (JAX, NumPyro, PyMC)
- Hands-on experience working with foundational models
- You have successfully fine-tuned LLMs
- Deep familiarity with the modern AI stack (HuggingFace, Unsloth, PyTorch, etc.)
Desired Qualifications
- Comfortable with agent-based modeling and economic simulation
- Familiarity with financial concepts (NAV, IRR, fund structures)
- PE experience
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