AI / ML Engineer

Throne, Inc.; Austin (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Throne, Inc. is seeking an experienced AI/ML Engineer to own challenging sensing and machine-learning problems from definition through deployment. You will oversee data and the ecosystem, drive high-leverage work, and shepherd solutions from concept to production.

You will own model performance across computer vision, video, audio, and next-generation sensing systems, and build the data, evaluation, and production infrastructure to improve performance over time.

Qualifications

• 7+ years of applied ML experience with production ownership.
• Experience training computer vision models across domains.
• Experience with pre-training or adapting base CV models.
• Strong PyTorch experience and debugging the full model stack.
• Model optimization: quantization, distillation, latency tuning, and cost tradeoffs.
• Strong SQL, Python, and data science skills with large datasets.
• Ability to work with ambiguous sensing problems using scientific methods.
• Familiar with modern LLM tools to scale research and coding.
• Self-starter who improves data, labeling, modeling, evaluation, and deployment.
• Strong communication and technical judgment for product impact.

Responsibilities

• Own end-to-end sensing/ML problems from definition to deployment with measurable outcomes.
• Improve CV/video model performance across classification, segmentation, detection, embeddings, and temporal understanding.
• Lead model training, including pre-training, fine-tuning, and transfer learning.
• Enhance model efficiency: quantization, distillation, pruning, latency and memory tradeoffs.
• Maintain rigorous evaluation: metrics, ablations, regression tests, and go/no-go criteria.
• Validate sensors for next-generation devices using scientific experiments.
• Work with data science SQL workflows to inspect production data and link to product.
• Integrate production ML with backend, firmware, and data systems for observability and reliability.
• Translate research into measurable product improvements in Throne's AI/ML ecosystem.
• Stay current with CV/video/multimodal/foundation-model research and apply ideas.

Skills

PyTorch

Python

SQL

Model optimization

Deployment

Experimentation

LLM tools

Communication

Tools

MLflow

W&B

Optuna

SageMaker

TorchServe

ONNX Runtime

TensorRT

Job description

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.

We're looking for an experienced AI / ML Engineer to own highly challenging sensing and machine-learning problems from definition through deployment. This is not a role where the roadmap will always arrive pre-scoped. You'll be expected to understand product objectives, oversee the data and the broader eco-system from which it emerges, identify the highest-leverage technical work, make sound tradeoffs, and drive the solutions you craft through implementation, validation, deployment, and continuous improvement.

You'll own model performance across computer vision, video, audio, and next-generation sensing systems, along with the data, evaluation, and production infrastructure required to improve that performance over time.

What You'll Own

• End-to-end solution ownership means taking loosely defined product or sensing problems, determining what needs to be learned or built, identifying critical unknowns, developing a plan, executing it, and driving the work to a measurable outcome.
• Computer vision and video model performance across classification, segmentation, detection, embeddings, and temporal/video understanding.
• Model training and pre-training, including fine-tuning, transfer learning, self-supervised or semi-supervised approaches. Pre-training base CV models experience is a strong plus.
• Model efficiency, including quantization, distillation, pruning, architecture selection, ONNX/TorchScript trade-off understanding, latency reduction, memory reduction, and cost/performance tradeoffs.
• Experimentation and evaluation rigor: metrics, ablations, cohort analysis, error taxonomies, regression tests, confidence calibration, and clear go/no-go criteria.
• Sensor validation for next-generation devices, using a scientific approach to compare sensors, characterize signal quality, design experiments, and determine whether new modalities improve real-world model performance.
• Data science and SQL workflows to inspect production data, evaluate model behavior, build analysis datasets, and connect model outcomes back to product and device behavior.
• Production ML integration, working with backend, firmware, and data systems to make models deployable, observable, reproducible, and reliable.
• Research translation, staying current with relevant CV, video, multimodal, compression, and foundation-model research and turning useful ideas into measurable improvements.

What We're Looking For

• 7+ years of applied ML experience, with strong production or research-to-production ownership.
• Deep experience training and improving computer vision models across multiple domains, not just using off-the-shelf APIs.
• Hands-on experience with pre-training or adapting base CV models, including dataset design, scaling tradeoffs, and representation quality.
• Strong PyTorch experience and comfort debugging the full model stack: data loading, augmentation, loss design, training stability, metrics, deployment export, and inference performance.
• Demonstrated experience with model optimization: quantization, distillation, efficient architectures, latency/cost tuning, and accuracy-efficiency tradeoffs.
• Strong SQL, Python, and data science skills for working directly with large datasets, labels, metrics, and model failure cases.
• Ability to work across ambiguous sensing problems with scientific discipline: form hypotheses, design experiments, control variables, and make evidence-backed recommendations.
• Capable of working well with modern LLM tools to scale research, coding, analysis, and experimentation efficiently.
• Self-starter mindset: able to improve the stack from data, labels, modeling, evaluation, deployment, and instrumentation rather than waiting for a narrowly scoped training task.
• Strong communication and technical judgment, especially around deciding what model work is likely to move real product performance.

Nice to Have

• Experience with video models, segmentation models, embeddings, temporal classification, multimodal learning, audio/signal processing, or sensor-fusion systems.
• Experience with Labeling tools (Supervisely, Roboflow, CVAT etc) MLflow / W&B, Optuna, SageMaker, TorchServe, ONNX Runtime, TensorRT, or similar ML infrastructure.
• Experience deploying models under edge, embedded, mobile, GPU, or strict latency/cost constraints.
• Experience with labeling pipelines, active learning, synthetic data, weak supervision, human review workflows, or model-assisted annotation.
• Experience validating new hardware sensors or working closely with firmware/device teams.

Why This Role Is Different

We are not looking for someone who is going to wait for a precisely scoped modeling problem. We are interested in candidates with deep experience owning AI/ML/CV solutions from problem to concept to production and beyond.

You might start with a question: "Why is this model underperforming?" or "Will this new sensor materially improve our ability to predict a clinical outcome?" From there, we expect you to architect and implement the program. Your efforts should include data collection, curation and analysis; failure mode investigation; labelling and managing third party labelers; designing experiments; updating/improving the models; modifying the inference pipeline; understanding production instrument behavior; and challenging legacy assumptions. All of this should be done with a cost-effective, production-quality product in mind. You will own Throne's AI/ML ecosystem.