Staff Machine Learning Engineer, Multi-Target Multi-Camera

Flock Safety is an all-in-one technology solution to eliminate crime and keep communities safe. Our intelligent platform combines the power of communities at scale - including cities, businesses, schools, and law enforcement agencies - to shape a safer future together. Our full-service, maintenance-free technology solution is trusted by communities across the country to help solve and deter crime in the pursuit of safer communities for everyone.

Our holistic public safety platform is comprehensive and intelligent, providing the actionable evidence needed to solve, deter and reduce crime across neighborhoods, schools, businesses and entire cities. Without compromising transparency or privacy, we are turning unbiased data into objective answers.

Flock strives to offer a career-defining experience where you can also make an impact on your community. While safety is a serious business, we are a supportive team that is optimizing the remote experience to create strong and fulfilling relationships even when we are physically apart. Our group of hard-working employees thrive in a positive and inclusive environment, where a bias towards action is rewarded. 

We have raised over $500M in venture capital from investors including Tiger Global, Andreessen Horowitz, Matrix Partners, Meritech Capital Partners, and Initialized Capital. Now surpassing a $5.5B valuation, Flock is scaling intentionally and seeking the best and brightest to help us meet our goal of reducing crime in the United States by 25% in the next three years.

Flock’s LPR and Video products detect and track objects to provide its customers with the best information to make actionable decisions. As a Staff Engineer in MTMC Tracking, you will play a critical role in architecting and deploying large-scale, real-time, and accurate multi-camera tracking solutions, advancing Flock beyond single-camera capabilities. You will work closely with research scientists, ML engineers, infrastructure teams, and operations to develop high-performance systems for tracking people, vehicles, or other objects across multiple cameras.

• 7+ years of industry experience in Deep Learning and Computer Vision

• Strong background in multi-target multi-object tracking

• Experience in metric learning, contrastive learning, and embedding-based ReID models

• Experience with integrating tracking algorithms like SORT, DeepSORT, ByteTrack, FairMOT, or graph-based tracking into systems

• Knowledge of probabilistic models (e.g., Kalman Filters, Bayesian filtering) and trajectory prediction.

• Experience in working with hybrid systems of deployed devices working with cloud processing

• Strong experience in Python

• Experience leading projects from R&D to production

• Experience with SQL

• Basic Git knowledge

• Basic Bash knowledge