Our client is an elite proprietary trading firm founded by MIT and Harvard alumni, consistently delivering 40%+ annual returns across equities and futures. Sitting at the intersection of top-tier academia and high-performance trading, it’s one of the most intellectually rigorous environments in the industry.
Nearly half of the firm’s roughly 40 people hold PhDs from the world’s leading institutions, and that academic depth is matched by cutting-edge execution. They’re now growing across engineering and research to build the next generation of their trading platform and strategies. This is a lean, high-density team where the code you write and the models you build run in production, and your decisions show up directly in PnL.
What you’ll do
Software Engineers & Quant Developers
Build and optimize the computational infrastructure behind core research and portfolio construction
Work side by side with researchers to turn new ideas into production trading systems
Design custom software to process large-scale market data in distributed and single-machine environments
Write high-performance, multi-threaded code using locks and lock-free primitives
Machine Learning Engineers
Build and scale the pipelines that train, evaluate, and deploy models on large volumes of market data
Turn research prototypes into fast, reliable models running in live trading
Optimize training and inference performance, from data loading to GPU and CPU efficiency
Develop tooling that helps researchers experiment faster and more rigorously
Researchers
Develop and test predictive signals and models across equities and futures
Apply statistics, machine learning, and optimization to noisy, real-world market data
Design rigorous experiments that separate real effects from overfitting
See your ideas go from hypothesis to live strategy, with clear feedback on what works
Across every role, you’ll own problems end to end with no layers between you and the result.
What you bring
Software Engineers & Quant Developers
Around 3+ years of software development experience, with strong C++ skills, low-level debugging, and resource optimization
Experience working in large, complex codebases behind critical systems
Machine Learning Engineers
Around 3+ years building and deploying ML systems in production
Strong Python skills and experience with a framework such as PyTorch; C++ is a plus
A solid grasp of ML fundamentals and how to make models fast and robust in practice
Researchers
A strong background in a quantitative field such as mathematics, statistics, physics, computer science, or operations research
Hands-on experience with statistical modeling or machine learning on real data
Comfort coding in Python or a similar language to test your own ideas
Everyone
A genuine love of hard technical problems: performance puzzles, clean abstractions, and understanding systems all the way down
Intense intellectual curiosity, whether that shows up in mathematics, chess, research, or building things for fun
Nice to have
A PhD in Computer Science, Mathematics, Physics, Statistics, Operations Research, or a related field (more common among researchers, though not required)
Strong results in competitions such as IMO, IPhO, IOI, Putnam, or competitive programming; many on the team have them, but it’s not a requirement
An exceptional academic or professional track record, wherever you earned it
Prior experience in trading, finance, or working with market data
Why this role
Rare talent density: a team of about 30, founded by MIT and Harvard alumni, where nearly half hold PhDs
Proven performance: a privately funded firm compounding at 40%+ per year
Direct impact: a flat structure with no middle management, where great work shows up immediately in results
Exceptional compensation: base salaries up to $550k, with fully guaranteed bonuses bringing total cash to $1.5M or higher.
Don’t tick every box? Please apply anyway. Outstanding engineers and researchers come from many paths, and we’d rather hear from you than have you rule yourself out.
Interested? Reply and Goliath Partners will be in touch. The team is small, so openings are limited.
