VOLT is building a new category of software & robotics products that will revolutionize the security industry and save human lives. Our solutions will be used in everyday environments to detect and mitigate risks such as mass-shootings, armed robberies, and other violent events.
We are backed by some of Silicon Valley’s leading investors and VC firms that were early-stage investors in Facebook, Uber, Boom, etc. Our founding team brings experience from Google, Apple, Facebook, Uber, Amazon, MIT.
We are looking for passionate, collaborative, and entrepreneurial individuals to join our team. If you like fast-paced environments, are passionate about AI/Robotics, and want to join a mission-driven work culture, then we’d love to speak to you!
As a founding engineer, you will help shape our product strategy, technical architecture, and deploy features that will keep millions of people safe.
- Design and implement real-time object tracking, classification, and reinforcement learning models for cloud and edge computing
- Create algorithms for object motion and trajectories predictions based on scene comprehension
- Implement deep learning models for estimating depth, positioning, motion, etc.
- Optimize models for low-latency on embedded hardware
- Building iterative learning models in the cloud
- Real experience building, training, and deploying deep neural networks with deep learning frameworks such as TensorFlow, Caffe, PyTorch, etc.
- Proficiency with Python or C++, preferably both
- Solid software engineering foundation and a commitment to writing clean, well-architected code
- Image processing experience with OpenCV or similar frameworks
- Experience with state-of-the-art in object detection, multi-object tracking, and segmentation
- Experience working on high-efficiency deep networks for real-time embedded systems
- Experience working on high-accuracy deep learning models in AWS and GCP
- Strong fundamental knowledge of linear algebra and projective geometry
- Data curation, annotation, and management experience
- TensorFlow, TensorFlow Lite