DataRobot
DataRobot Benefits Overview
Compensation + Benefits
Offers 401(K)
Offers life insurance
Offers disability insurance
Offers supplemental life insurance
Offers company equity
Offers dental insurance
Offers health insurance
Offers mental health benefits
Offers dependent care
Offers Flexible Spending Account (FSA)
Offers vision insurance
Offers Health Savings Account (HSA)
Offers generous parental leave
Work-Life Balance + Wellbeing
Offers company-sponsored outings
Offers generous PTO
Provides paid sick days
Provides paid holidays
Offers sabbatical leave
Career Growth + Development
Provides customized development tracks
Job training & conferences
Company Culture
Offers a remote work program
Provides free snacks and drinks
Recently posted jobs
Artificial Intelligence • Information Technology • Machine Learning • Software
Conduct research engineering for a probabilistic foundation model spanning temporal, tabular, and mixed-modality data. Design model architectures, encoders, attention mechanisms, distributional output heads, and decoding strategies; implement and train scalable PyTorch models; run controlled experiments, ablations, and performance profiling; diagnose training behavior; and produce reproducible, production-quality code. Candidates with stochastic modeling expertise may also develop synthetic data generators and simulate richer stochastic dynamics.
Artificial Intelligence • Information Technology • Machine Learning • Software
As a Staff Software Engineer, you will lead the technical vision for DataRobot's Fleet team, mentoring engineers and architecting scalable infrastructure using Kubernetes for multi-cloud environments.
Artificial Intelligence • Information Technology • Machine Learning • Software
Own the product roadmap for AI workload and agent runtime orchestration across Kubernetes clusters and heterogeneous accelerators. Responsibilities include deployment APIs, workload placement, capacity, autoscaling, isolation, networking, governance, metering, reliability, and auditability. The role spans two engineering pods and requires deep expertise in Kubernetes, GPUs, distributed systems, multi-tenancy, platform APIs, and technical prototyping, with influence across matrixed teams and no direct reports.
