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Strong employer brand and metro location increase competition, but niche senior ML research reduces applicant density.
Requires trading/commodity domain knowledge alongside ML research, so skills are moderately transferable across industries.
Explicit advanced ML research requirements, specialized skills, and a 10-year experience expectation raise filter strictness.
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Design and conduct advanced generative-AI research experiments (fine-tuning, reinforcement learning, world models) to discover and validate new trading signals.
Evaluate and validate complex and novel datasets, partnering across teams to identify and prioritize data capture and analytical tool development for trading opportunities.
Build reproducible research pipelines, benchmarks, and evaluation frameworks to rigorously measure signal quality and robustness, and communicate findings for productionization.
Bachelor's degree in a related field or equivalent experience.
Demonstrated experience conducting applied machine-learning or generative-AI research, including model fine-tuning and/or reinforcement learning.
Experience with agricultural trading business and strong understanding of valuable trading information.
Significant experience with relational and distributed data systems and data visualization tools.
Experienced in advanced machine learning research with hands-on skills in generative AI, reinforcement learning, and fine-tuning large language models.
Strong programming skills especially in Python and familiarity with modern ML frameworks such as PyTorch or JAX.
Domain expertise in commodity and financial markets with a track record of applying AI/ML methods to financial or trading signal generation.