Principal Software Engineer

## Principal Software Engineer Apply locations:
Boston, Massachusetts, US:
Seattle, Washington, US:
San Francisco, California, UStime type:
Full time posted on:
Posted Todayjob requisition :
R-102682
*
* Job Description:

** Data Robot 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 aRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on Data Robot 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 Data Robot'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) into the engine
* Optimize for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators
* Build and maintain instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations
* Develop and enhance scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads
* Integrate with federated, distributed inference infrastructure – orchestrate across nodes, balance load, handle communication overhead
* Collaborate cross-functionally: with platform engineers, cloud infrastructure, and security/compliance teams
* Document and share learnings, contributing to internal best practices and open-source efforts when possible###
***
* What We’re Looking For:

***** 10+ years of engineering experience, with at least 5+ in infrastructure, platform, or backend systems roles.
* Deep expertise in Kubernetes internals and operations, including networking, scheduling, scaling, and controller patterns.
* Proven ability to design and build systems from scratch, making pragmatic tradeoffs along the way.
* Strong proficiency in modern programming languages such as Python or Go. Experience building production-quality, reliable, and observable systems that are used across engineering organizations.
* A growth-oriented mindset—driven to teach, learn, and improve systems as well as people.
* Experience operating across multiple cloud providers (AWS, GCP, Azure) and/or hybrid environments.
* Strong experience with Helm, container orchestration patterns, and CI/CD automation.
* Comfortable working with IaC (Terraform, Pulumi) and Git Ops workflows.
* Ability to influence without authority and align diverse stakeholders around technical decisions
**** Nice to Have:
***** Familiarity with Cilium, Kyverno, KEDA, Gateway API, OPA, or similar technologies.
* Experience building and running multi-tenant SaaS platforms.
* Exposure to on-prem delivery models or regulated environments.
* Experience with…