Deep Learning Research Engineer Intern

This is a fully remote position, open to applicants in California, +2 more states.

📋 Description

• Design and enhance the fundamental architecture of probabilistic foundation models.

• Construct input encoders for temporal, unordered tabular, and mixed-modality data types.

• Develop attention factorizations that span variables, rows, and time horizons.

• Create distributional output heads and decoding strategies for generating coherent joint samples.

• Conduct controlled studies on architecture, including ablations, scaling analyses, and profiling of memory and throughput.

• Develop scalable implementations in PyTorch that accommodate larger input/output dimensions, enhance throughput, and optimize memory usage.

• Investigate the interplay between architecture, data generation, inference constraints, benchmark quality, and practical applicability.

• Enhance synthetic-data engines with more complex stochastic dynamics, constraints, dependence structures, heavy tails, and regime behaviors.

• Transform research concepts into reliable implementations and credible empirical findings.

⛳️ Requirements

• Proficient in PyTorch with hands-on experience in constructing and training deep learning models.

• Strong grasp of Transformers, attention mechanisms, and long-context or state-space sequence architectures, including the trade-offs between quality, memory, and latency.

• Experience in probabilistic modeling within neural networks, including distributional output heads, likelihood-based or proper-scoring-rule losses, mixture or flow models, or related uncertainty-aware learning frameworks.

• Solid foundation in probability and statistics.

• Capability to translate architectural concepts into functioning GPU-native implementations, controlled experiments, and diagnostics.

• Strong engineering practices, such as writing readable code, creating tests, conducting reproducible experiments, and maintaining disciplined evaluations.

• Skill in debugging training instability and iterating from hypotheses to evidence.

• Working knowledge of stochastic processes and stochastic differential equations.

• Experience in designing synthetic data generators or simulation-based training programs.

• Depth of knowledge in a domain characterized by rich stochastic structures, such as finance, energy, or commodities.

• Familiarity with tabular or mixed-modality deep learning approaches.

• Position intended for individuals at a post-doctoral level or very near to it.

🏝️ Benefits

• Medical, Dental & Vision Insurance.

• Flexible Time Off Program.

• Paid Holidays.

• Paid Parental Leave.

• Global Employee Assistance Program (EAP).