Flock - Engineering Manager, Computer Vision

• 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 • 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. • Onboard technically • 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. • Establish relationships • Deep-dive into the ML lifecycle at Flock, gaining hands-on experience with our data and model workflows. • Deep-dive into the ML lifecycle • Develop a clear baseline of the team’s current work-in-flight to provide leadership with visibility into project status, goals, and estimated timelines. • Develop a clear baseline • 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. • Participate in the customer resolution cycle • Identify and triage technical debt, surfacing at least three meaningful bottlenecks in data/modeling infrastructure or security. • Identify and triage technical debt • Balance execution with delegation by personally resolving high-priority improvements while coaching team members through delegated technical challenges. • Balance execution with delegation • Raise the bar on operational hygiene by auditing Jira workflows and security boards to ensure the team is moving efficiently and securely. • Raise the bar on operational hygiene • 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. • Fully own the long-term roadmap • Lead the quarterly planning process, synthesizing feedback from your team and stakeholders into a prioritized list of high-leverage ML initiatives. • Lead the quarterly planning process • Cultivate a culture of continuous improvement by establishing repeatable processes for identifying technical gaps and exploring new R&D opportunities. • Cultivate a culture of continuous improvement • Champion professional development within the org, managing performance review cycles and team participation. • Champion professional development