Principal Engineer - AI Platform & Operations

Role Description

We are looking for a Principal Engineer to serve as the technical expert for our AI Platform & Operations. This isn’t just a role about maintaining infrastructure; it’s about owning the technical vision for the foundational systems that will power every AI product team across the company.

Remote Role - can be based in either Washington or California State.

By building robust, self-serve internal developer platforms, you’ll enable our engineers to deploy, monitor, and scale AI models with unprecedented efficiency and safety. Your work ensures that our "Agentic" future is reliable, cost-effective, and cutting-edge.

What You’ll Be Doing

• Architect the Future: Define the long-term technical roadmap for our AI platform, covering everything from model serving and feature stores to experiment tracking and CI/CD for ML.
• Set the Gold Standard: Establish engineering benchmarks for deployment, versioning, A/B testing, and automated rollbacks.
• Optimize & Scale: Lead strategies for GPU/compute efficiency and cost optimization while managing the complexities of LLM inference (quantization, batching, and latency) at scale.
• Enhance Observability: Design sophisticated monitoring and alerting systems specifically tailored for AI workloads in production.
• Champion Reliability: Drive platform stability and partner with application teams to ensure our infrastructure meets evolving product needs.
• Technical Leadership: Mentor Senior engineers, lead architecture reviews, and evaluate the next generation of cloud services and ML frameworks.

Qualifications

• Senior Expertise: 12+ years of engineering experience, with at least 4 years at a Principal or Distinguished level.
• ML Infrastructure Mastery: Expert-level knowledge of model serving (e.g., Triton, vLLM, Ray Serve) and deep experience with Kubernetes and cloud ecosystems (AWS/GCP/Azure).
• The MLOps Toolkit: Proven experience with tools like MLflow, Weights & Biases, or Kubeflow.
• Coding & Systems: High proficiency in Python and a "systems-thinking" approach to Docker, Helm, and containerization.
• AI Specialization: Hands-on experience operating LLM inference at scale and a deep understanding of the trade-offs between throughput and latency.
• Platform Mindset: A track record of building internal platforms that treat other engineers as the primary customer, drastically improving engineering velocity.

Benefits

• A competitive base pay
• Medical Benefits
• Discretionary incentive plan based on individual and company performance
• Focus on development: Access to a learning & development platform and opportunity for you to own your career pathways
• Flexible work policy

The pay range is between $168,000 - $230,000 USD as a base salary offering annually.