Flock - Engineering Manager, Mulitimodal Modeling

• 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. • Feeling uneasy that you haven’t ticked every box? That’s okay; we’ve felt that way too. Studies have shown women and minorities are less likely to apply unless they meet all qualifications. We encourage you to break the status quo and apply to roles that would make you excited to come to work every day. • 90 Days at Flock • 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. • The First 30 Days • 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 • The First 60 Days • 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. • 90 Days & Beyond • 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.