About Throne

Throne is a continuous tracking device for getting personalized insight about gut health and hydration. Co-founded by John Capodilupo — co-founder and former CTO of WHOOP — we're bringing the rigor of continuous health tracking to two signals that have long been a guessing game. Our north star is to improve health and save lives.

Principal Engineer

We’re looking for a Principal Machine Learning Systems Engineer to own technically challenging problems spanning machine learning, data systems, backend infrastructure, and production software.

This is a hands-on role for an experienced engineer who thrives when the problem is important and the path to solving it is unclear. You will be expected to understand the objective, investigate the system and data, identify the highest-leverage work, make sound architectural and modeling decisions, and drive solutions from initial investigation through production deployment and operation.

At Throne, the boundary between ML and software engineering is intentionally porous. A model-performance problem may turn out to be a labeling problem, a data-pipeline problem, an inference architecture problem, or a product instrumentation problem. We want someone who is comfortable following the problem wherever it leads.

Location

Austin, Texas (In-person)

What You’ll Own

• End-to-end technical problem solving. Tackle ambiguous product and engineering problems by identifying the critical questions, determining what needs to be built or learned, and driving the work to a measurable outcome.
• Production ML systems. Build, train, evaluate, optimize, deploy, and monitor models across computer vision, video, audio, and other sensing modalities.
• Backend architecture. Design and operate production services and APIs supporting device data, customer-facing applications, ML inference, and internal systems.
• Large-scale media and ML pipelines. Own data ingest, object storage, queueing, worker orchestration, indexing, inference, dataset generation, and downstream metric computation.
• Dataset and evaluation systems. Develop strategies for data mining, labeling, dataset quality, split design, active learning, model evaluation, regression testing, and failure analysis.
• Model performance and efficiency. Drive improvements through model architecture, training strategy, transfer learning, quantization, distillation, inference optimization, and cost/performance tradeoffs.
• Cloud infrastructure and deployments. Build and operate scalable infrastructure across compute, storage, queues, databases, networking, secrets, and model-serving systems.
• Data architecture. Design schemas and data flows that support both product workloads and high-volume ML/data-science workflows.
• Production reliability. Establish observability, metrics, alerting, capacity planning, incident response, rollout safeguards, and model-performance monitoring.
• Sensor and algorithm experimentation. Work with hardware, firmware, product, and R&D teams to evaluate new sensing modalities and determine whether they create meaningful improvements in system performance.