Who We Are

Founded in 2021, we are building the best business AI video system on the market. Powered by next-generation video artificial intelligence, we deliver unprecedented insights and a 10x better user experience than the incumbents of the vast but stagnant video security industry.

Our customers range from warehouses, schools, hospitals, hotels, and many more. As we grow rapidly, we’re looking for someone to join our team to help us scale our systems and deliver new features.

Team You Will Work With

Our company was founded by serial entrepreneurs and experts in AI and robotics. Our engineering team includes industry veterans with experience at companies like Lyft, Google, Zoox, Toyota, Facebook, Microsoft, and academic institutions including Stanford, Oxford, and Cornell. Our go-to-market team includes leaders with experience from top security and technology companies. We are venture-backed, revenue-generating, and have a solid financial runway.

Joining our team means working on challenging problems at the intersection of user experience, machine learning, and infrastructure. It also means committing to technical excellence, continuous learning, and fast-paced delivery.

The Role: Machine Learning Engineer

We are hiring a Machine Learning Engineer to fine-tune and productionize open-source PyTorch models, optimize them for performance, and integrate them into our core product.

Responsibilities:

• Fine-tune existing open-source PyTorch models and deploy them in a C++ runtime environment.
• Use in-house datasets to refine model performance.
• Design and conduct experiments to assess trade-offs between latency and accuracy.
• Integrate models into real-world use cases and take ownership of performance metrics.
• Maintain and enhance all ML applications currently in production.
• Stay current with research and implement relevant developments in video security use cases.

Requirements:

• Strong software engineering skills with a focus on writing production-grade code.
• Solid machine learning fundamentals (e.g., linear algebra, probability, supervised/self-supervised learning).
• Keen interest in deep learning research and the latest developments in foundation models and LLMs.
• (Nice to have) Experience productionizing PyTorch models in C++, including profiling and optimization for latency.
• Good understanding of Docker and containerization.
• (Nice to have) Experience with PyTorch, Python 3, and C++.
• (Nice to have) Familiarity with TorchScript, ONNX Runtime, or TensorRT.
• (Nice to have) Understanding of inference optimization techniques such as half-precision and INT8 quantization.