at Flock Safety
Full TimeFlock 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.
As a Computer Vision Engineer specializing in image processing with a machine learning focus, you will design and implement efficient computer vision algorithms for tasks such as image stacking, noise reduction, super-resolution, and multi-camera fusion, leveraging machine learning techniques where applicable. Your work will focus on creating lightweight solutions that can be deployed on edge devices with limited computational resources. You will collaborate with a team of engineers to develop and deploy robust, real-time imaging solutions.
8+ years of experience in the field of computer vision
Master's degree in Computer Science, Electrical Engineering, or a related field.
Strong understanding of computer vision principles and techniques, with a focus on multi-frame and multi-camera processing.
Proficiency in programming languages such as Python and C++.
Strong expertise in machine learning.
Experience training, optimizing, and deploying machine learning models.
Strong problem-solving and analytical skills.
Experience with image processing libraries such as OpenCV or Pillow.
Understanding of Image Signal Processing (ISP) principles and techniques.
Experience working with raw image data and camera sensors.
Experience with version control systems such as Git.
Excellent communication and collaboration skills.
Experience in optimizing algorithms for edge devices.
Preferred Qualifications:
Experience with embedded systems and real-time computer vision.
Experience with hardware acceleration of ISP pipelines and machine learning models.
Knowledge of camera calibration techniques and tools for multi-camera systems.
Experience with low power programming.
Experience with specific edge computing platforms (e.g., Qualcomm, NVIDIA Jetson, Raspberry Pi).
Experience with model quantization and pruning.
Ramp up: Meet with key stakeholders and gain an understanding of the project scope and goals.
Learn how to navigate tools, workflows, and collaboration processes.
Conduct a literature review on relevant ML techniques, especially for thermal + RGB fusion.
Set up and validate the development environment with initial tests on existing algorithms.
Take ownership of your assigned projects and define clear next steps.
Begin implementation of ML models, focusing on thermal + RGB fusion.
Deliver initial results and analyze areas for improvement.
Evaluate additional ML techniques for broader image processing applications.
Strengthen collaboration with internal teams and external partners to align on project direction.
Communicate progress regularly, highlighting risks and proposing mitigation strategies.
Identify potential optimizations to improve workflow, tools, or technical approaches.
Finalize a proof of concept for thermal + RGB fusion, demonstrating a working model with real-world datasets.
Explore feasibility on target hardware platforms and define a roadmap for deployment.
Continue refining ML techniques, incorporating feedback to enhance performance.
Work closely with stakeholders to align future improvements with business and technical goals.
Evaluate successes and areas for growth, adjusting strategies for long-term impact.
Flock is an equal opportunity employer. We celebrate diverse backgrounds and thoughts and welcome everyone to apply for employment with us. We are committed to fostering an environment that is inclusive, transparent, and collaborative. Mutual respect is central to how Flock operates, and we believe the best solutions come from diverse perspectives, experiences, and skills. We embrace our differences and know that we are stronger working together.
If you need assistance or an accommodation due to a disability, please email us at careers@flocksafety.com. This information will be treated as confidential and used only to determine an appropriate accommodation for the interview process.
At Flock Safety, we compensate our employees fairly for their work. Base salary is determined by job-related experience, education/training, as well as market indicators. The range above is representative of base salary only and does not include equity, sales bonus plans (when applicable) and benefits. This range may be modified in the future. This job posting may span more than one career level.
Yearly based
Tampere
