Principal Software Engineer

Company: DataRobot

Location: Boston, MA

Type: Full-time

Posted: 2026-05-18

About this role

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.

As a Principal Software Engineer, you’ll be responsible for technical leadership and vision. You’ll lead by example—rolling up your sleeves as a technical contributor to solve complex problems, shaping architecture, and mentoring engineers to do their best work and advance their careers. You’ll work across our control plane systems, influence cross-team roadmaps, and bring pragmatic engineering practices into how we build, test, and operate infrastructure software.

This is not a “stay in your swim lane” role. You’ll question assumptions, challenge complexity, and help drive a high-performance culture. You’ll be trusted to bring clarity where there’s ambiguity, and momentum where there’s inertia.

This role includes participation in an on-call rotation—we believe in shared ownership of our platform and aim to build systems that are resilient, observable, and require minimal intervention.

What You’ll Do:

  • You will help design, develop, and optimize the inference engine that powers DataRobot's agentic infrastructure API., ensuring large language model (LLM) serving systems are fast, scalable, and efficient. Your work will touch the full GenAI inference stack — from kernels and runtimes to orchestration and memory management.
  • Contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference
  • Collaborate with partners such as NVIDIA to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) int...

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