Staff Machine Learning Engineer, Multimodal Modeling

Staff Machine Learning Engineer, Multimodal Modeling WHO IS FLOCK? Flock Safety 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. THE OPPORTUNITY As a Staff Machine Learning Engineer, Multimodal Modeling you will lead the advancement of our core embedding-based retrieval systems, with a primary focus on the scientific aspects of modeling. This includes fine-tuning and extending multimodal models (e.g., CLIP, SigLIP) to improve performance, generalization, and cross-modal alignment. You’ll work on unifying text and image representations, improving model performance, and ensuring extensibility across evolving product use cases. Your work will be central to Flock’s ability to deliver fast, accurate, and scalable search experiences powered by state-of-the-art vision-language systems. THE SKILLSET - 7+ years of industry experience in Machine Learning with a focus on representation learning, multimodal modeling, or embedding-based retrieval. - Deep domain knowledge in at least one area: computer vision, natural language processing, or recommendation systems. - Strong proficiency in PyTorch, with experience fine-tuning foundation models and adapting pretrained vision-language models to real-world tasks. - Demonstrated ability to customize and extend model architectures, training loops, loss functions, and data pipelines to deliver impact. - Experience with embedding-based retrieval, including contrastive learning, multimodal alignment, and designing evaluation methods for vector similarity search and embedding quality. - Solid engineering fundamentals in Python, with familiarity in Git, SQL, and Bash. - Comfortable working independently and navigating ambiguity, with a track record of solving open-ended modeling problems. BONUS IF YOU HAVE - Familiarity with model compression techniques, such as distillation, quantization, and architecture pruning, to improve inference efficiency and deployability. - Experience with vector search infrastructure, including provisioning, maintaining, and querying large-scale vector databases (e.g., FAISS, Weaviate, Pinecone) - Proficient with multi-GPU and distributed training workflows, to scale training of large multimodal models efficiently 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 prescribe to 90 day plans and 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 were to be hired as a Staff Machine Learning Scientist at Flock Safety. The First 30 Days - Meet the team & cross-functional stakeholders - Understand the system architecture for freeform search and the ownership of the various components - One major cultural component within Flock’s engineering teams is the “first day push”. The first day push focuses setup and o ... (truncated, view full listing at source)