ML Engineer

Focus

Build and ship the machine learning pipelines that give our devices real-time perception and intelligence, from prototype to production at scale.

• 3–6 years of experience working with AI models in production, ideally for real-time applications.
• Fluent in Python for data pipelines and backend features involving third-party model providers’ APIs and SDKs.
• Well-versed in a variety of data stores and their tradeoffs: relational DBs, No-SQL, cloud object stores, etc.
• Experience with modern ML frameworks (PyTorch/TensorFlow), and experience optimizing models for edge/embedded inference is a plus, but not required.
• Build data pipelines and evaluation frameworks to continuously improve AI feature quality from real-world usage.
• Own the full lifecycle: data collection, dataset versioning, evaluation design, deployment and monitoring in the field, and feature improvement recommendations.
• Comfortable with cloud infrastructure.
• Ability to move fast in an environment where things are sometimes loosely defined, and a genuine curiosity about where AI meets physical products.