Senior ML Systems Architect

Throne, Inc.; Austin (TX); On-site; USD 210,000 - 290,000; Full time

Job summary

Throne is seeking a Principal Machine Learning Systems Engineer to own technically challenging problems across ML, data systems, backend infrastructure, and production software in Austin, TX. This hands-on role demands deep investigation, architectural decisions, and driving production deployment and operation.

The role blends ML and software engineering with responsibilities from data pipelines to model-performance monitoring, emphasizing end-to-end ownership, experimentation, and scalable

Qualifications

• 7+ years of professional engineering experience spanning software, ML engineering, data infrastructure, or related disciplines.
• Experience owning complex technical problems from loosely defined objectives through production outcomes.
• Strong Python skills and substantial experience with production ML frameworks such as PyTorch.
• Strong backend engineering experience building distributed production systems; Go is highly desirable.
• Experience deploying and operating ML inference workloads in production; SQL/PostgreSQL fundamentals.

Responsibilities

• Own end-to-end ML systems spanning computer vision, video, audio, and other sensing modalities.
• Design and operate production services and APIs for device data, ML inference, and internal systems.
• Build and optimize large-scale data pipelines, storage, queuing, and dataset generation.
• Drive model evaluation, active learning, regression testing, and failure analysis.
• Improve model performance and cost efficiency via architecture, training, quantization, and distillation.

Skills

Python

PyTorch

Go

SQL

Data pipelines

System design

Cloud AWS

ML engineering

Tools

Terraform

PostgreSQL

Kafka

SageMaker

Job description

Throne is seeking a Principal Machine Learning Systems Engineer to own technically challenging problems across ML, data systems, backend infrastructure, and production software in Austin, TX. This hands-on role demands deep investigation, architectural decisions, and driving production deployment and operation.

The role blends ML and software engineering with responsibilities from data pipelines to model-performance monitoring, emphasizing end-to-end ownership, experimentation, and scalable