Deep Learning Research Engineer Intern

DataRobot

. Design and improve the core probabilistic foundation-model architecture .

Posted 10/13/2026internshipRemote • Massachusetts • United StatesEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing probabilistic models using PyTorch, with a strong foundation in probability and statistics. Capable of developing scalable architectures and conducting controlled experiments to enhance model performance and real-world applicability.

Highest-signal resume keywords

PyTorch ProficiencyProbabilistic ModelingTransformers and Attention VariantsGPU-Native ImplementationsSynthetic Data Generation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningProbabilistic ModelingAttention MechanismsStatistical ReasoningArchitectural DesignControlled ExperimentsDebugging Training InstabilityStochastic ProcessesSimulation-Based TrainingReadable Code

Soft Skills

CollaborationEngineering HabitsIterative Problem Solving

Tools & Technologies

PyTorchGPU Implementations

Industry Keywords

FinanceEnergyCommoditiesQuantitative Analysis

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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 tradeoffs between quality, memory, and latency
Experience with probabilistic modeling in neural networks, including 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, including reasoning about densities and masses, dependence between variables, and calibration
Ability to connect architectural ideas to working GPU-native implementations, controlled experiments, and diagnostics
Strong engineering habits, including readable code, tests, reproducible experiments, and disciplined evaluation
Ability to debug training instability, reason about changed results, and iterate from hypothesis to evidence
Working knowledge of stochastic processes and stochastic differential equations is a strong plus
Experience designing synthetic data generators or simulation-based training curricula is a strong plus
Depth in finance, energy, commodities, or a similarly quantitative field is a strong plus
Familiarity with tabular or mixed-modality deep learning is a strong plus
Post-doctoral-level experience or very close to it
PyTorch proficiency
Ability to work with GPU-native implementations

Benefits

Comp & perks

Medical, Dental & Vision Insurance
Flexible Time Off Program
Paid Holidays
Paid Parental Leave
Global Employee Assistance Program (EAP)
Competitive pay and benefits