About the role#
This is a research-engineering internship for individuals operating at or near the post-doctoral level. You will shape the architecture of a probabilistic foundation model designed to handle temporal forecasting, unordered tabular regression, classification, missing-data completion, and mixed-modality inputs. The project aims to create a joint structure across variables, rows, horizons, and scenarios to solve real-world problems.
What you'll do#
- Design and improve core model architecture, including input encoders, attention mechanisms, and distributional output heads.
- Run controlled architecture studies to inform design decisions.
- Build scalable PyTorch implementations that optimize throughput and memory usage.
- Study the interactions between architectural choices, data-generation, and inference constraints.
- Extend the synthetic-data engine with richer stochastic dynamics and dependence structures.
- Translate research ideas into robust implementations and credible empirical results.
What you'll need#
- Strong PyTorch skills and hands-on experience building and training deep models.
- A solid understanding of Transformers, attention variants, and long-context or state-space sequence architectures.
- Experience with probabilistic modeling in neural networks, such as distributional output heads or likelihood-based losses.
- A strong foundation in probability and statistics.
- The ability to connect architectural ideas to GPU-native implementations and diagnostics.
- Strong engineering habits, including readable code, tests, reproducible experiments, and disciplined evaluation.
- The ability to debug training instability and iterate quickly.
- A background in Computer Science, Data Science, Mathematics, Statistics, Physics, or Computer Engineering.
- A working knowledge of stochastic processes and stochastic differential equations, experience designing synthetic data generators, or familiarity with tabular/mixed-modality deep learning are strong pluses.
Location & details#
- This is a full-time, paid internship.
- Work is available on a remote or on-site basis in Boston, Massachusetts.
- The term is rolling.
About DataRobot
DataRobot provides an agent workforce platform for enterprise AI development. The company offers tools for model development, MLOps, and governance. It supports AI experimentation and deployment across various cloud environments. Founded in 2012, the firm is based in Boston and employs roughly 880 people.
How to get in at DataRobot
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