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Throne is a continuous tracking device company co-founded by John Capodilupo (co-founder and former CTO of WHOOP) focused on personalized gut health and hydration insights through continuous monitoring. The company aims to improve health and save lives by bringing rigorous health tracking to signals that have historically been guesswork.
You will 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 problems are important and solutions are unclear. You will understand objectives, investigate systems and data, identify high-leverage work, make sound architectural and modeling decisions, and drive solutions from investigation through production deployment and operation.
At Throne, the boundary between ML and software engineering is intentionally porous. A model-performance problem may be a labeling issue, data-pipeline issue, inference architecture problem, or product instrumentation problem. The ideal candidate is comfortable following problems wherever they lead.
Key responsibilities include: end-to-end technical problem solving on ambiguous product and engineering challenges; building, training, evaluating, optimizing, deploying, and monitoring production ML models across computer vision, video, audio, and sensing modalities; designing and operating production services and APIs for device data, customer applications, ML inference, and internal systems; owning large-scale media and ML pipelines including data ingest, object storage, queueing, worker orchestration, indexing, and metric computation; developing dataset and evaluation strategies including data mining, labeling, quality assurance, active learning, and failure analysis; driving model performance and efficiency improvements through architecture, training strategy, transfer learning, quantization, and inference optimization; building and operating scalable cloud infrastructure; designing data schemas and flows supporting both product and ML workflows; establishing production reliability through observability, alerting, capacity planning, and incident response; collaborating with hardware, firmware, product, and R&D teams on sensor and algorithm experimentation; and identifying architectural weaknesses and scalability constraints to drive technical direction.
REQUIREMENTS:
- 7+ years of professional engineering experience spanning software engineering, ML engineering, data infrastructure, or closely related disciplines
- Track record of 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
- Experience personally training and improving ML models (not simply integrating third-party APIs)
- Strong understanding of ML evaluation, dataset construction, failure analysis, experimentation, and model-performance tradeoffs
- Experience building large-scale asynchronous or data-processing systems using object storage, queues, worker fleets, and databases
- Strong SQL and relational-database fundamentals; PostgreSQL experience preferred
- Strong cloud infrastructure experience, ideally AWS and infrastructure-as-code (Terraform)
- Experience deploying and operating ML inference workloads in production
- Strong software architecture and API-design instincts
- Ability to reason scientifically about ambiguous technical problems
- Exceptional technical independence
- Strong communication skills and ability to work across backend, ML, firmware, hardware, product, and R&D teams
- Comfortable using modern AI/LLM development tools
NICE TO HAVE:
- Computer vision experience (classification, segmentation, detection, embeddings, video/temporal models)
- Audio or signal-processing experience
- Multimodal or sensor-fusion experience
- Model optimization using ONNX, TensorRT, quantization, distillation
- SageMaker or other ML infrastructure experience
- Experience with labeling systems, active learning, weak supervision, or model-assisted annotation
- High-volume media processing experience
- Connected devices or embedded systems experience
- Hardware sensor validation experience
- Experience operating systems with significant cost-per-inference or cloud-cost constraints
- Experience inheriting and simplifying complicated systems
