Patrick Bradley

I'm a Solutions Engineer at a leading ML dataset labeling company based in San Francisco. For the past year and a half, I have specialized in the Autonomous Vehicle, Robotics, and AR/VR industries. Day to day, I handle both the technical sales process as well as the engineering required to spin up 3D Perception data labeling pipelines.

On the sales process side of the job, this requires creating customized demos of the labeling platform for new engagements, directly leading the end-to-end labeling and software platform proof-of-concepts or Pilots, and working with the Account Executive to influence technical business terms in labeling contracts that range from six to seven figures.

On the technical side, I am subject matter expert on ML dataset building for 2D & 3D Perception as well as high definition Maps. I technically advise the Perception teams on topics directly related to data representation, including localization, camera calibration, Lidar data handling, and time synchronization. I will then write the integration code to manipulate the Sensor Fused data primitives into a format required by our set of APIs, and assist in setting up stable pipelines for labeling. I also help maintain and improve our publicly facing data manipulation Python SDK.

I started out as a BDC at Oracle doing pure sales development. However, I took the initiative upon myself to build up knowledge around Machine Learning first as a Product Management Intern for Oracle Machine Learning. Using this initial foundation in classical ML algorithms, I reached out to a CalTech scientist working on the classification of neural signals from the brain indicative as attention. After landing this ML Engineering internship outside of my day job, I spent my evenings learning how to really code with python in a collaborative setting, how to read research papers and implement my learnings into real applications, and learning how to architect models using TensorFlow. We inevitably built a prototype for an digital art exposition that would change the visual dynamics of the presented digital visual based upon the brain activity of the participant.

To get closer to machine learning, I found work as a Solutions Engineer at Silicon Valley's largest private data labeling company, throwing myself directly into the weeds of 3D Computer Vision with applications in the Autonomous Vehicle, Robotics, and AR/VR Industries. In this work, I've learned how to sell in large Enterprise commercial settings, how to run pilots exceptionally, and ensure that end business terms are suitable. Technically, I"ve grown tremendously, becoming a subject matter expert in both data labeling and AV, Robotics, and AR/VR ML use cases. Additionally, I'm doing profession Python engineering work for massive pipelines, and contributing to public Software Development kits.

I studied Cognitive Science at UCLA - most of the foundations in Machine Learning, especially perception, is extremely influential in the modern day implementation of Neural Networks and Computer Vision. I took some computer science courses, as well as statistics as well. 

However, most of my knowledge base has been the result of a career path that I took the initiative on, diving deep into the weeds in new ambitious topics, and benefiting technically.