Machine Learning Researcher - Build Models That Move Markets

Thurn Partners Ltd; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Thurn Partners Ltd. seeks a senior Machine Learning Researcher to join a quantitative trading team focused on moving P&L through rigorous modelling. You will develop and vet models using Python, C++, PyTorch and deep learning, working with elite researchers on petabyte-scale data.

The role requires a PhD or Master’s in a quantitative field, 6+ years in software, and proven impact from models in finance or related domains. US work eligibility is essential.

Qualifications

• Senior-level experience leading a team or working closely with one.
• Proven track record of models with real, demonstrable impact.
• Experience in finance and modeling for alpha or P&L improvement.

Responsibilities

• Develop and improve ML models with a focus on P&L impact.
• Apply statistics, ML, DL, and NLP to large, proprietary datasets.
• Lead or contribute to end-to-end model development from concept to test.
• Iterate quickly using fast feedback loops on live data.
• Collaborate with other experts to shape strategic trading outcomes.

Skills

Python

C++

PyTorch

Education

PhD or Master’s in CS/ML/Statistics or related

Job description

A quantitative trading firm is hiring a senior Machine Learning Researcher to work at the centre of financial trading itself, analyzing systematic trading data and building models alongside some of the strongest technical talent in the industry. The team you'd join is built from people who came out of serious research environments, PhDs and postdocs from places like MIT and other top research universities, who made the leap from academic or industrial research into quant trading and now apply that same rigour to markets. This is a hands-on role for someone who has already led real work, not just contributed to it, where every idea gets tested against hard problems and judged by one standard alone: does it move P&L. If you want to see your models shape real trading outcomes rather than sit in a benchmark, and want to do that work alongside people who've made that same jump themselves, this is that kind of seat.

What you'll do:

• Develop and improve machine learning models as part of a small, technical team, with your work judged on one clear standard: whether your model moves P&L.
• Use statistics, machine learning, deep learning, and NLP to extract patterns from various kinds of datasets through innovative and rigorous research.
• Lead or work closely alongside a team, bringing your own ideas and driving them from concept through to a working, tested model.
• Test ideas against real feedback quickly, this is not an environment where a model waits months to find out if it worked.
• Work with proprietary data at petabyte scale that exists only within this firm, unseen by any outside model or team.

Your profile:

• Senior-level experience, either leading a team directly or working in close technical partnership with one, with a track record of models you've built that had real, demonstrable impact.
• Strong proficiency in Python, C++, and PyTorch.
• At least 6 years working in the software industry.
• A PhD or Master's degree in computer science, machine learning, statistics or a related quantitative field.
• Must be eligible to work in the United States.
• Published research, open-source contributions, or other public evidence of original technical work, though shipped, unpublished results carry equal weight.
• Experience in the financial sector, ideally having worked closely with a Portfolio Manager or trading desk to develop models that sharpened alpha or directly improved P&L.

Pre-Application:

• Your application is subject to our privacy policy (thurnpartners.com/privacy-policy).

Why this role:

Competitive compensation, a feedback loop measured against real trading outcomes rather than a slow release cycle, and access to petabyte-scale proprietary data no other firm or model has ever touched. This is technical leadership on models that matter immediately, not research that sits in a queue.