[Hiring] Deep Learning Research Engineer Intern @DataRobot

Role Description

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future.

This effort is about building a multi-target joint probabilistic foundation model that can be used across industries to tackle some of the hardest real-world problems. The ambition goes beyond what tabular and time-series foundation models usually do:

• One model should support temporal forecasting, unordered tabular regression and classification, missing-data completion, and mixed-modality inputs and outputs.
• Learning coherent joint structure across connected variables, rows, horizons, and scenarios.
• Creating real value in serious use cases rather than optimizing benchmark scores in isolation.

This role is designed as a research-engineering internship for someone operating at post-doctoral level, or very close to it, who wants to shape the architecture of a probabilistic foundation model. The center of gravity is the model itself:

• The encoders that read temporal, tabular, and mixed-modality inputs.
• The attention and sequence mechanisms that carry structure across variables, rows, and horizons.
• The distributional output heads and decoding schemes that turn hidden states into coherent joint predictions.

We are looking for someone who can reason precisely about what an architecture can and cannot represent, and who then implements, trains, and evaluates the resulting PyTorch models in a production-quality codebase.

Qualifications

• 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.
• Experience with probabilistic modeling in neural networks.
• Strong foundation in probability and statistics.
• Ability to connect architectural ideas to working GPU-native implementations.
• Strong engineering habits: readable code, tests, reproducible experiments, and disciplined evaluation of model changes.
• Ability to debug training instability and iterate quickly from hypothesis to evidence.

Requirements

• Working knowledge of stochastic processes and stochastic differential equations.
• Experience designing synthetic data generators or simulation-based training curricula.
• Depth in a domain with rich stochastic structure such as finance, energy, or commodities.
• Familiarity with tabular or mixed-modality deep learning.

Benefits

• Medical, Dental & Vision Insurance.
• Flexible Time Off Program.
• Paid Holidays.
• Paid Parental Leave.
• Global Employee Assistance Program (EAP).
• And more!

Company Description

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.

DataRobot is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

DataRobot uses approved AI-powered tools to support the hiring process in selected regions. All decisions regarding applications are made by members of the DataRobot team.