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Senior specialized GenAI/RL trading role reduces pool, but global firm increases applicant interest.
Applied GenAI, RL, and trading-signal expertise with commodity market knowledge limits cross-industry transferability.
Senior level, explicit 10+ years preference, advanced ML research skills and domain experience create strict filters.
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Design and execute advanced generative-AI research experiments to discover and validate new trading signals, emphasizing fine-tuning, reinforcement learning, and world models.
Evaluate and prioritize complex, novel datasets across global teams to enhance data quality and trading insights.
Build reproducible research pipelines and benchmarks; communicate findings to guide productization of trading signals.
Bachelor's degree in a related field or equivalent experience.
Demonstrated experience in applied machine-learning or generative-AI research including model fine-tuning and/or reinforcement learning.
Experience in agricultural trading business and understanding of valuable trading information.
Significant experience with relational and distributed data systems and data visualization tools.
10+ years working with data and data warehousing systems, preferably with financial or trading signal applications.
Advanced degree (MS/PhD) in machine learning, computer science, statistics, physics or related quantitative field preferred.
Strong expertise in generative AI methods (including fine-tuning LLMs), Python, modern ML frameworks (PyTorch, JAX), and familiarity with sequence/time-series modeling in noisy, non-stationary environments.