Principal Machine Learning Engineer

CONFIDENTIAL Scovai; Seattle (WA); Hybrid; USD 180,000 - 340,000; Full time

Job summary

Scovai in Seattle is seeking a Principal Machine Learning Engineer to act as the senior technical authority for ML systems in a hybrid environment. This is an individual-contributor role focused on setting technical direction without people management.

You will design and build large-scale training and inference systems, own model architecture decisions for high-impact ML products, and drive MLOps practices across the engineering organization, optimizing latency, cost, and reliability.

Qualifications

• Experience shipping large-scale ML systems in production.
• Ability to define deployment, monitoring and retraining standards for ML pipelines.
• Strong collaboration with applied science and platform teams across the organization.

Responsibilities

• Design and build large-scale training and inference systems.
• Own model architecture decisions for high-impact ML products.
• Drive best practices for experimentation, evaluation, and MLOps.
• Mentor senior and staff engineers and participate in architecture reviews.
• Evaluate build-vs-buy decisions for ML infrastructure and stay current with state-of-the-art DL techniques.

Skills

ML system design

Production ML engineering

MLOps

Model deployment

Latency optimization

Tools

GPU clusters

Feature stores

Open-source ML frameworks

Job description

We are conducting a confidential search for a Principal Machine Learning Engineer to serve as the senior technical authority for ML systems within a technology organization based in Seattle, working hybrid. This is an individual-contributor role for an engineer who has shipped large-scale ML systems in production and wants to set technical direction without moving into people management.

In this role, you will design and build large-scale training and inference systems, own model architecture decisions for high-impact ML products, and drive best practices for experimentation, evaluation, and MLOps across the engineering organization. You will work closely with applied science and platform teams to take models from research prototype to production at scale, optimizing for latency, cost, and reliability.

You will mentor senior and staff engineers, set technical standards for model deployment, monitoring, and retraining pipelines, and represent ML engineering in architecture reviews. You will evaluate build-vs-buy decisions for ML infrastructure, stay current on the state of the art in deep learning and LLM systems, and translate emerging techniques into practical, production-grade improvements.

Nice to Have:

• Experience deploying and scaling large language model inference infrastructure
• Contributions to open-source ML frameworks or published research
• Experience with distributed training on GPU clusters at scale
• Background in feature stores and real-time inference systems
• Track record of setting technical direction across multiple engineering teams