Deep Learning Research Engineer Intern

DataRobot; Sacramento (CA); On-site; USD 120,000 - 160,000; Part time

Job summary

DataRobot is creating a multi-target probabilistic foundation model to address complex real-world problems. This internship targets researchers near post-doctoral level who can shape the model encoders reading temporal, tabular, and mixed-modality data, and who will implement, train, and test the PyTorch-based solutions in a production-quality codebase.

The role emphasizes precise reasoning about architectural capabilities, with a focus on practical value over mere benchmarks, and includes

Qualifications

• Post-doctoral level research experience in ML or AI.
• Ability to implement, train, and evaluate PyTorch models in production-grade code.
• Strong background in stochastic modeling is a plus.

Responsibilities

• Design and prototype a probabilistic foundation model architecture.
• Implement, train, and evaluate models with robust experimentation.
• Collaborate across research and engineering to ship production-ready code.

Skills

Post-doctoral level

Research

PyTorch

Probabilistic modeling

Tools

PyTorch

Job description

Job 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 while learning coherent joint structure across connected variables, rows, horizons, and scenarios. The primary objective is to create real value in serious use cases rather than optimize benchmark scores in isolation, although strong results on standard benchmarks are expected to follow closely.

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, and 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. A background in stochastic modeling is a strong plus rather than a prerequisite: the model is trained on synthetic data drawn from stochastic dynamics, so a candidate