Machine Learning Researcher

The D. E. Shaw group seeks a machine learning researcher to conduct quantitative research using modern machine learning (ML) techniques. In this role, the researcher will develop and evaluate novel modeling approaches and explore how advances in ML can be applied to complex problems in systematic investing, with the opportunity to own research from initial hypothesis through implementation. The role combines deep ML and quantitative research with strong technical execution, including solving computational challenges when necessary to pursue ambitious research efficiently and at scale.

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What you’ll do day-to-day

• Develop and evaluate ML models and approaches for challenging quantitative research problems.
• Formulate research hypotheses and design rigorous experiments to assess predictive performance, robustness, and generalization.
• Explore and apply advances in ML research, including new model architectures, representations, training objectives, and learning techniques.
• Rapidly prototype, implement, and benchmark promising ideas using large-scale financial and other complex datasets.
• Develop efficient implementations and tackle computational challenges—including large-scale training or GPU optimization—where doing so enables more ambitious quantitative research.
• Collaborate with quantitative researchers and developers to take successful ideas from initial exploration toward real-world application.

What we’re looking for

• Bachelor’s degree or higher.

• Deep expertise in ML and a sound quantitative research orientation, with experience developing and empirically evaluating sophisticated ML approaches.

• Solid understanding of modern ML methods and the mathematical and statistical principles underlying them.

• Demonstrated ability to formulate hypotheses, design rigorous experiments, interpret results, and distinguish meaningful improvements from noise or overfitting.

• Excellent programming skills and the ability to independently take research ideas from concept through scalable implementation.

• Proficiency with modern ML frameworks such as PyTorch or JAX; experience with large-scale training, GPU computing, performance optimization, or technologies such as CUDA, XLA, or Triton is valuable for more computationally intensive research.

• Intellectual curiosity, keen research judgment, and an interest in applying advances in ML to difficult quantitative problems.

• The expected annual base salary for this position is $275,000 to $350,000. Our compensation and benefits package includes substantial variable compensation in the form of a year-end bonus, guaranteed in the first year of hire, a sign-on bonus, and benefits including medical and prescription drug coverage, 401(k) contribution matching, wellness reimbursement, family building benefits, and a charitable gift match program.