Required Skills
Strong PyTorch skills and hands-on experience building and training deep models.
Solid understanding of Transformers, attention variants, and long-context or state-space sequence architectures, including the tradeoffs between quality, memory, and latency.
Experience with probabilistic modeling in neural networks: distributional output heads, likelihood-based or proper-scoring-rule losses, mixture or flow models, or related uncertainty-aware learning setups.
Strong foundation in probability and statistics, enough to reason about densities and masses, dependence between variables, and calibration.
Ability to connect architectural ideas to working GPU-native implementations, controlled experiments, and diagnostics that show why a change helped.
Strong engineering habits: readable code, tests, reproducible experiments, and disciplined evaluation of model changes.
Ability to debug training instability, reason about why results changed, and iterate quickly from hypothesis to evidence.
Strong Plus
Working knowledge of stochastic processes and stochastic differential equations: drift and diffusion, jumps, regime switching, mean reversion, heavy tails, and how such dynamics are simulated numerically.
Experience designing synthetic data generators or simulation-based training curricula, and an understanding of how the training distribution shapes what a model learns.
Depth in a domain with rich stochastic structure such as finance, energy, commodities, or a similarly quantitative field.
Familiarity with tabular or mixed-modality deep learning: categorical, ordinal, count, and bounded targets alongside continuous ones.
What You Can Expect To Work On
Designing and improving the core model architecture: input encoders for temporal, unordered tabular, and mixed-modality data; attention factorizations across variables, rows, and horizons; the distributional output heads and decoding strategies that produce coherent joint samples.
Running controlled architecture studies, from ablations and scaling behavior to memory and throughput profiles, and turning the results into design decisions.
Building scalable PyTorch implementations that support larger input and output spaces, better throughput, and tighter memory budgets.
Studying how architectural choices, data-generation choices, and inference constraints interact to change benchmark quality and real-world usefulness.
For candidates with the stochastic-modeling background: extending the synthetic-data engine with richer stochastic dynamics, constraints, dependence structures, heavy tails, and regime behavior.
Turning research ideas into robust implementations and credible empirical results.
What You Should Expect
Work that sits close to the core of the project rather than at the edges.
A fast research loop where good ideas can move quickly from hypothesis to implementation to benchmark.
A real chance to contribute to research that aims for top-tier, state-of-the-art outcomes rather than only incremental internal work.
A team that values rigorous thinking, clean code, and practical usefulness at the same time.
Exposure to problems that combine deep learning, stochastic modeling, and real deployment constraints instead of isolating only one of those dimensions.
A role that fits someone with broad interests who wants to work across both advanced modeling and serious implementation work rather than staying narrowly specialized.
The talent and dedication of our employees are at the core of DataRobot’s journey to be an iconic company. We strive to attract and retain the best talent by providing competitive pay and benefits with our employees’ well-being at the core. Here’s what your benefits package may include depending on your location and local legal requirements: Medical, Dental & Vision Insurance, Flexible Time Off Program, Paid Holidays, Paid Parental Leave, Global Employee Assistance Program (EAP) and more!
DataRobot Operating Principles
Wow Our Customers
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Be Better Than Yesterday
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Use of Artificial Intelligence in Our Hiring Process
DataRobot uses approved AI-powered tools to support the hiring process in selected regions. These tools may assist in writing job descriptions, reviewing applications, assessing qualifications, and evaluating candidate materials. All decisions regarding applications are made by members of the DataRobot team.
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