Engineering Manager, Mulitimodal Modeling

Flock

1d•$200,000 - $220,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.

Benefits

Flexible PTO: We offer non-accrual PTO, plus 11 company holidays.
Fully-paid health benefits plan for employees: including Medical, Dental, and Vision and an HSA match.
Family Leave: All employees receive 12 weeks of 100% paid parental leave. Birthing parents are eligible for an additional 6-8 weeks of physical recovery time.
Fertility & Family Benefits: We have partnered with Maven, a complete digital health benefit for starting and raising a family. Flock will provide a $50,000-lifetime maximum benefit related to eligible adoption, surrogacy, or fertility expenses.
Spring Health: Spring Health offers a variety of mental health benefits, including therapy, coaching, medication management, and digital tools, all tailored to each individual's needs.
Caregiver Support: We have partnered with Cariloop to provide our employees with caregiver support
Carta Tax Advisor: Employees receive 1:1 sessions with Equity Tax Advisors who can address individual grants, model tax scenarios, and answer general questions.
ERGs: We want all employees to thrive and feel like they belong at Flock. We offer four ERGs today - Women of Flock, Flock Proud, LEOs and Melanin Motion. If you are interested in talking to a representative from one of these, please let your recruiter know.
WFH Stipend: $150 per month to cover the costs of working from home.
Productivity Stipend: $300 per year to use on Audible, Calm, Masterclass, Duolingo and so much more.
Home Office Stipend: A one-time $750 to help you create your dream office.