Senior Software Engineer, AI Solutions
General MotorsFull Time
Mid-level (3 to 4 years), Senior (5 to 8 years)
Salary: $175K - $200K Employment Type: Full-Time Location Type: Remote
Flock Safety is an all-in-one technology solution dedicated to eliminating crime and enhancing community safety. Our intelligent platform leverages the collective power of cities, businesses, schools, and law enforcement agencies to build a safer future. We provide a comprehensive, maintenance-free technology solution trusted nationwide to help solve and deter crime, ultimately creating safer communities for everyone.
Our holistic public safety platform is designed to be comprehensive and intelligent, delivering actionable evidence to solve, deter, and reduce crime across neighborhoods, schools, businesses, and entire cities. We achieve this without compromising transparency or privacy, transforming unbiased data into objective answers.
Flock aims to provide a career-defining experience where you can make a tangible impact on your community. While safety is our core business, we foster a supportive team environment that optimizes the remote experience, cultivating strong and fulfilling relationships despite physical distance. Our dedicated employees thrive in a positive and inclusive culture that rewards a bias towards action.
We have secured over $700M in venture capital funding from prominent investors including Tiger Global, Andreessen Horowitz, Matrix Partners, Bedrock Capital, Meritech Capital Partners, and Initialized Capital. With a valuation exceeding $7.5B, Flock is undergoing intentional scaling and is seeking top talent to help us achieve our goal of reducing crime in the United States by 25% within the next three years.
As a Sr. Embedded ML Engineer, you will play a crucial role in developing the software that powers Machine Learning on our rapidly expanding fleet of deployed devices. You will collaborate closely with our Device, Imaging, and ML Modeling teams. Your responsibilities will include the design, development, testing, and productionization of our embedded systems and their associated applications. This will involve contributing to hardware requirement determination, expanding the scope of evidence capture, and enhancing the quality and speed of evidence delivery to our customers.
You will work in tandem with technical leaders to implement system components and develop robust testing frameworks. These frameworks will be essential for validating the functionality, performance, and integration of our Kotlin-based embedded codebase. While experience with model development or optimization is not a prerequisite for this role, a deep understanding of embedded systems and software quality assurance through automated testing is essential.
We understand that you might not meet every single qualification. If you're excited about this role and believe you can make a significant contribution, we strongly encourage you to apply. We value diverse perspectives and are committed to building a team where everyone feels empowered to succeed.
We operate with a results-oriented culture, and we believe traditional job descriptions are a thing of the past. Our focus is on driving tangible outcomes.
License plate reader cameras for crime prevention
Flock Safety provides a system aimed at enhancing public safety through crime prevention while ensuring privacy and reducing bias. The main product is a network of license plate reader cameras that capture essential vehicle information, which helps in solving crimes. These cameras utilize machine learning technology to ensure that the data collected is objective and ethically used. Flock Safety serves a variety of clients, including neighborhoods, businesses, and law enforcement agencies in over 1,000 cities. The company operates on a subscription model, where clients pay for the installation, maintenance, and access to data and analytics. This approach not only generates a steady revenue stream but also allows clients to benefit from ongoing updates and support. Flock Safety's goal is to create safer communities by providing effective crime prevention tools that respect individual privacy and foster trust between the public and law enforcement.