Machine Learning Researcher - Build Models That Move Markets

Thurn Partners Ltd; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

Thurn Partners Ltd. is seeking a Senior Machine Learning Researcher to join at the center of financial trading. You will develop models that directly affect P&L and work with top-tier researchers in a hands-on environment.

You bring 6+ years in software or quant research, strong Python and C++, and expertise in PyTorch. A PhD or MS in a quantitative field is preferred, with eligibility to work in the United States without sponsorship.

Qualifications

• Senior-level experience leading a team or working in close technical partnership.
• Proven track record of models with real, demonstrable impact on outcomes.
• Eligible to work in the United States without sponsorship.

Responsibilities

• Develop and improve machine learning models for trading with measurable P&L impact.
• Collaborate with a Portfolio Manager to shape models that sharpen alpha.
• Lead or contribute to end-to-end model development from concept to tested model.
• Work with petabyte-scale proprietary data within the firm.

Skills

Python

C++

PyTorch

Education

PhD or Master’s in CS/ML/Stats

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.
• Work closely with a Portfolio Manager to shape models that sharpen alpha and improve trading outcomes, if you come from financial services, though this experience is preferred, not required.
• 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:

• You must be eligible to live and work in the US, without requiring sponsorship.
• 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.