Research Engineer

• Design and improve the core probabilistic foundation-model architecture
• Develop input encoders for temporal, unordered tabular, and mixed-modality data
• Design attention factorizations across variables, rows, and horizons
• Develop distributional output heads and decoding strategies for coherent joint samples
• Run controlled architecture studies, including ablations, scaling behavior, memory, and throughput profiling
• Build scalable PyTorch implementations supporting larger input/output spaces, improved throughput, and tighter memory budgets
• Study how architecture, data generation, and inference constraints affect benchmark quality and real-world usefulness
• Extend the synthetic-data engine with richer stochastic dynamics, constraints, dependence structures, heavy tails, and regime behavior when applicable
• Turn research ideas into robust implementations and credible empirical results
• Collaborate on research targeting state-of-the-art outcomes and practical deployment constraints

Explore Deep Learning Research Engineer Intern at DataRobot. Review requirements and application details.