Flock is the leading safety technology platform, helping communities thrive by taking a proactive approach to crime prevention and security. Our hardware and software suite connects cities, law enforcement, businesses, schools, and neighborhoods in a nationwide public-private safety network. Trusted by over 5,000 communities, 4,500 law enforcement agencies, and 1,000 businesses, Flock delivers real-time intelligence while prioritizing privacy and responsible innovation.
We’re a high-performance, low-ego team driven by urgency, collaboration, and bold thinking. Working at Flock means tackling big challenges, moving fast, and continuously improving. It’s intense but deeply rewarding for those who want to make an impact.
With nearly $700M in venture funding and a $7.5B valuation, we’re scaling intentionally and seeking top talent to help build the impossible. If you value teamwork, ownership, and solving tough problems, Flock could be the place for you.
Flock is a mission-driven public safety company building hybrid hardware-software systems to help eliminate crime. Our LPR and video devices collect vast volumes of information, powering products that help law enforcement and communities search, interpret, and act on critical intelligence. Flock provides investigators with tools that support text-to-image, image-to-image, and semantic search, increasing the speed and precision of investigations.
As an Engineering Manager, ML Vision, you will lead a team of high-performing engineers responsible for advancing and maintaining Flock’s core license plate recognition and vehicle intelligence models. Your team will drive innovation in computer vision modeling, improving accuracy, robustness, and generalization across diverse real-world conditions. You will set the technical direction and execution strategy for model development, evaluation, deployment, and lifecycle management, ensuring our systems perform reliably at scale.
As a people leader, you will be responsible for hiring, mentoring, and growing a strong, mission-driven engineering team. You will foster a culture of technical excellence, ownership, and continuous learning, supporting individual growth and long-term career development. You will balance forward-looking innovation with operational excellence, ensuring our production systems remain performant, resilient, and extensible.
In close partnership with product, hardware, and platform teams, you’ll align roadmap priorities with business impact while empowering your team to execute effectively. Your leadership will be central to Flock’s ability to deliver accurate, real-time vehicle intelligence that powers critical public safety workflows.
Lead and grow a high-performing ML Vision team while remaining hands-on in the design, development, and optimization of Flock’s license plate recognition and vehicle intelligence models.
Architect and guide improvements in model accuracy, robustness, and generalization across diverse real-world conditions, hardware variations, and edge cases.
Drive innovation in vehicle and license plate modeling, extending capabilities to support new data sources and evolving product needs.
Oversee and actively participate in the full ML lifecycle — data strategy, training pipelines, validation, deployment, monitoring, and retraining — ensuring scalable, reliable production systems.
Balance forward-looking innovation with maintenance and operational excellence, proactively addressing model drift, technical debt, and system reliability.
Partner cross-functionally with product, hardware, and platform teams to translate business priorities into a clear technical roadmap and deliver high-impact vehicle intelligence capabilities.
Mentor and develop engineers through technical leadership, code reviews, architectural guidance, and structured feedback, fostering a culture of ownership and continuous improvement.
Drive continuous improvement in the team's engineering and ML development processes, actively removing roadblocks to enable faster innovation and execution.
7+ years of industry experience in Machine Learning, with deep expertise in computer vision and deploying models in real-world production systems.
Experience leading engineers through formal management or strong technical mentorship, with a track record of growing individuals and elevating team performance.
Strong hands-on proficiency in PyTorch and modern deep learning workflows, including model training, fine-tuning, evaluation, and failure analysis.
Demonstrated ownership of ML initiatives end-to-end — from data strategy and experimentation to deployment, monitoring, and iterative improvement in production.
Experience improving model robustness and generalization across diverse environmental conditions, hardware variability, and operational edge cases.
Solid engineering fundamentals in Python, with experience building reliable, maintainable systems and contributing to shared infrastructure.
Comfortable balancing hands-on technical contributions with roadmap alignment, cross-functional collaboration, and team development in a fast-moving environment.
Experience building or deploying computer vision systems for real-world edge environments (e.g., traffic cameras, embedded systems, mobile or drone imagery).
Familiarity with large-scale model retraining workflows, model drift detection, and continuous evaluation in production ML systems.
Experience improving inference efficiency through model compression techniques (e.g., distillation, quantization, pruning) or hardware-aware optimization.
Exposure to multi-GPU or distributed training workflows for scaling training of large vision models.
Experience leading technical roadmap planning or scaling teams through periods of rapid product growth.
90 Days at Flock
First 30 Days - learn the business, build credibility
Onboard technically by completing a "first-day push" and setting up a local ML development environment to understand the team’s current developer experience.
Establish relationships and regular 1:1 cadences with your direct reports (Enrichment and Maintenance teams) and meet cross-functional partners in Product and Data Engineering.
Deep-dive into the ML lifecycle at Flock, gaining hands-on experience with our data and model workflows.
Develop a clear baseline of the team’s current work-in-flight to provide leadership with visibility into project status, goals, and estimated timelines.
First 60 days - take ownership, raise the bar
Participate in the customer resolution cycle by personally resolving technical issues and model performance gaps to understand the end-user experience.
Identify and triage technical debt, surfacing at least three meaningful bottlenecks in data/modeling infrastructure or security.
Balance execution with delegation by personally resolving high-priority improvements while coaching team members through delegated technical challenges.
Raise the bar on operational hygiene by auditing Jira workflows and security boards to ensure the team is moving efficiently and securely.
90 Days and beyond - drive scale, influence outcomes
Fully own the long-term roadmap and operational health of the ML Enrichment and Maintenance teams, ensuring alignment with broader company goals.
Lead the quarterly planning process, synthesizing feedback from your team and stakeholders into a prioritized list of high-leverage ML initiatives.
Cultivate a culture of continuous improvement by establishing repeatable processes for identifying technical gaps and exploring new R&D opportunities.
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