Companies DataRobot

DataRobot

Boston, Massachusetts, USA

1,610 Total Employees

Year Founded: 2012

View Website

Jobs at Similar Companies

Similar Companies Hiring

Idler

Artificial Intelligence

San Francisco, California

6 Employees

Fairly Even

Software • Sales • Robotics • Other • Hospitality • Hardware

2 Offices

Bellagent

Artificial Intelligence • Machine Learning • Business Intelligence • Generative AI

3 Offices

20 Employees

What It's Like to Work at DataRobot

Updated on March 05, 2026

This page summarizes recurring themes identified from responses generated by popular LLMs to common candidate questions about DataRobot and has not been reviewed or approved by DataRobot.

What's it like to work at DataRobot?

Strengths in product scope, external credibility, and compensation are accompanied by recurring concerns about restructuring risk, rapid strategic shifts, and uneven leadership effectiveness. Together, these dynamics suggest a reputation that can be attractive for impact- and learning-oriented candidates, but one where role and team due diligence materially affects expected experience.

Positive Themes About DataRobot

Innovation & Products: The work is framed as real, applied enterprise AI across predictive, generative, and agentic capabilities, with broad exposure across the ML lifecycle. Platform visibility and ongoing shipping/partner activity are presented as sources of interesting customer problems and resume-relevant scope.
Recognition: External recognition is highlighted through mentions like Fortune’s Future 50 and Gartner Magic Quadrant positioning. This is portrayed as boosting credibility and signaling market relevance.
Compensation: Pay is portrayed as competitive, with compensation described as a relative strength compared with other aspects of the experience. This is positioned as aligning with “brand-name AI” compensation expectations.

Considerations About DataRobot

Job Insecurity: A history of layoffs and restructurings is emphasized, with repeated references to 2022 reductions and ongoing volatility concerns. This is presented as a risk factor that warrants team-by-team validation before joining.
Change Fatigue: Frequent strategy shifts and repositioning across classical ML, gen-AI, and agentic directions are described as common. This is framed as energizing for some profiles but disorienting and stressful for others.
Leadership Gaps: Leadership churn, uneven management sentiment, and communication growing pains are described as recurring concerns. The result is portrayed as inconsistent experiences across teams and less predictable org maturity.

NEW

What does AI tell candidates about your employer brand?

Get your free AI reputation report today.

See AI Report

The insights on this page are generated by submitting structured prompts to some of the most popular large language models (“LLMs”) and summarizing recurring themes from the responses. Because the insights are generated using AI, they may contain errors. The insights do not necessarily reflect internal data, employee interviews, or verified company information. They may be influenced by incomplete, outdated, or inaccurate data, and may vary across LLM providers. These insights are intended for informational purposes only and should not be interpreted as a factual or definitive assessment of a company's reputation. Built In makes no representations or warranties regarding the accuracy, completeness, or reliability of this information, and disclaims any liability for any actions taken based on this information. If you are a representative of this company, and would like this page to be removed, you may contact us via this form.