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.
As an Engineering Manager, Multimodal ML, you will lead a team of high-performing engineers responsible for advancing Flock's core multimodal search and retrieval systems. Your team builds and maintains the embedding models that power image-to-vector pipelines, embedding storage and retrieval infrastructure, and cross-modal search capabilities enabling both image and text queries. You will also oversee the moderation model that ensures safe and appropriate query handling across our search products.
You will set the technical direction and execution strategy for multimodal model development, evaluation, deployment, and lifecycle management — ensuring our retrieval systems deliver fast, accurate, and scalable search experiences. As a people leader, you will be responsible for hiring, mentoring, and growing a strong, mission-driven engineering team, fostering a culture of technical excellence, ownership, and continuous learning.
In close partnership with product, search, 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 state-of-the-art vision-language systems that power critical public safety workflows.
Lead and grow a high-performing Multimodal ML team — hiring top talent, developing engineers' careers, and building a culture of ownership, technical excellence, and continuous improvement.
Manage competing workstreams across model development, production reliability, and moderation — prioritizing effectively and ensuring the team delivers on the highest-impact work.
Guide the development and improvement of multimodal models for image-text and image-to-image embeddings, ensuring high-quality modality alignment and retrieval performance.
Oversee the full ML lifecycle for multimodal systems — data strategy, training pipelines, evaluation frameworks, vector storage, deployment, monitoring, and retrieval — ensuring scalable, reliable production systems.
Drive innovation in cross-modal search capabilities, extending support to new data sources (e.g., drone video, mobile LPR imagery) and evolving product needs.
Own the moderation model, ensuring query handling meets safety and appropriateness standards across search products.
Own production reliability for the multimodal search pipeline, including incident response, on-call processes, and service-level commitments.
Balance forward-looking innovation with maintenance and operational excellence, proactively addressing model drift, embedding quality degradation, and system reliability.
Partner cross-functionally with product, search, data engineering, and platform teams to translate business priorities into a clear technical roadmap and deliver high-impact multimodal capabilities.
Mentor and develop engineers through technical leadership, code reviews, architectural guidance, structured feedback, and regular performance cycles — fostering individual growth and long-term career development.
3+ years of engineering management experience leading Machine Learning teams, with a track record of hiring, developing, and retaining high-performing engineers.
7+ years of industry experience in Machine Learning, with strong familiarity with computer vision, multimodal modeling, or embedding-based retrieval systems.
Demonstrated experience managing competing workstreams — balancing innovation, production reliability, and maintenance across multiple concurrent initiatives.
Experience running structured performance cycles, including goal-setting, regular feedback, calibration, and career development conversations.
Proven ability to hire effectively — sourcing, interviewing, calibrating, and closing candidates for technical ML roles.
Strong technical foundation in Python and modern ML workflows, with enough depth to evaluate architectural decisions, review code, and guide experimentation strategy.
Experience owning production ML systems end-to-end, including deployment, monitoring, incident response, and iterative improvement.
Familiarity with classification or content moderation systems, including understanding of safety filtering, policy enforcement, and evaluation of model outputs.
Comfortable partnering cross-functionally with product, search, data engineering, and platform teams to align roadmap priorities with business impact.
Hands-on experience with multimodal models (e.g., CLIP, SigLIP, BLIP), including fine-tuning, evaluation, and failure analysis.
Deep familiarity with embedding-based retrieval concepts, including contrastive learning, cross-modal alignment, and evaluation methods for vector similarity search.
Experience with embedding storage and retrieval infrastructure at scale, such as vector search built on columnar databases (e.g., Clickhouse) or purpose-built vector stores (e.g., FAISS, Weaviate, Pinecone).
Experience with model compression techniques (e.g., distillation, quantization, pruning) to improve inference efficiency and deployability at scale.
Exposure to multi-GPU or distributed training workflows for scaling training of large multimodal models.
Experience scaling teams through periods of rapid product growth.
We are a results-oriented culture and believe job descriptions are a thing of the past. We prescribe 90-day plans because we believe that good days lead to good weeks, which lead to good months. This serves as a preview of the 90-day plan you will receive if you are hired in this role.
Establish relationships with your team and who they work with, understanding the product, ongoing projects, and what success looks like
Pair with your team to get a hands-on overview of the end-to-end R&D lifecycle for multimodal models.
Lead project management for the team
Get involved with recruiting for your team by participating in interviews
Lead how the production inference pipeline and search backend work.
Work on a project as an individual contributor to get first hand experience of the R&D experience and how to improve it for your team.
Identify and resolve technical or process inefficiencies to improve team velocity and system reliability.
Mentor reports on breaking down complex initiatives into clear plans with well-scoped milestones.
Establish standard operating procedures (SOPs) for the model lifecycle, including data curation, evaluation, and deployment.
Lead quarterly planning to align team priorities with business impact and complete your first team performance review cycle.
In this role, you’ll recei
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