Jobs at DataRobot

Provides technical leadership for scalable Kubernetes-based infrastructure across multi-cloud and hybrid environments. Architects control plane services, tenancy models, deployment pipelines, observability, and reliability capabilities. Drives modernization through GitOps, policy-as-code, networking, autoscaling, CI/CD, and container lifecycle practices. Collaborates with SRE, platform, and application teams, mentors engineers, influences technical roadmaps, participates in on-call rotations, and improves infrastructure performance, resilience, and developer experience.

Generate and qualify new-logo opportunities for Account Executives by researching target accounts, conducting multi-channel outbound outreach, uncovering business needs, scheduling discovery meetings, and maintaining accurate CRM records. The role collaborates with Sales and Marketing to build pipeline for DataRobot’s generative AI platform and applications, while meeting activity and conversion targets.

Build and improve AI agent systems focused on Model Context Protocol and memory. Work includes developing MCP servers and clients, integrating tools and memory into agent loops, experimenting with context retrieval, testing behavior, documenting implementations, and shipping iterative improvements. The role emphasizes Python development, curiosity about agent systems, clear communication, collaboration, and learning through hands-on experimentation.

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.

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.