Staff Machine Learning Scientist

Position Overview

• Location Type: Remote
• Job Type: Full-Time
• Salary: $200K - $240K

Flock Safety is a technology company dedicated to eliminating crime and enhancing community safety. Our platform integrates cities, businesses, schools, and law enforcement to create safer futures. We provide a comprehensive, maintenance-free technology solution trusted by communities nationwide to deter and solve crime. Our intelligent public safety platform offers actionable evidence to reduce crime across neighborhoods, schools, businesses, and cities, utilizing unbiased data for objective answers without compromising transparency or privacy. Flock offers a career-defining experience with a positive and inclusive remote work environment that fosters strong relationships. We encourage a bias towards action and reward hard work. Having raised over $700M in venture capital and achieving a $7.5B valuation, Flock is scaling intentionally to reduce crime in the United States by 25% in the next three years.

The Opportunity

As a Staff Machine Learning Engineer, Multimodal Modeling, you will be instrumental in advancing our core embedding-based retrieval systems, with a strong emphasis on the scientific aspects of modeling. Your responsibilities will include fine-tuning and extending multimodal models (e.g., CLIP, SigLIP) to enhance performance, generalization, and cross-modal alignment. You will focus on unifying text and image representations, improving model performance, and ensuring the extensibility of our systems for future product use cases. Your contributions will directly impact Flock's ability to deliver fast, accurate, and scalable search experiences powered by state-of-the-art vision-language systems.

Requirements

• 7+ years of industry experience in Machine Learning, specializing in representation learning, multimodal modeling, or embedding-based retrieval.
• Deep domain knowledge in at least one of the following areas: computer vision, natural language processing, or recommendation systems.
• Strong proficiency in PyTorch, including experience with fine-tuning foundation models and adapting pre-trained vision-language models for real-world applications.
• Proven ability to customize and extend model architectures, training loops, loss functions, and data pipelines to achieve impactful results.
• 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 with Git, SQL, and Bash.
• Ability to work independently, navigate ambiguity, and a track record of solving open-ended modeling problems.

Bonus Qualifications

• Familiarity with model compression techniques such as distillation, quantization, and architecture pruning to enhance inference efficiency and deployability.
• Experience with vector search infrastructure, including provisioning, maintaining, and querying large-scale vector databases (e.g., FAISS, Weaviate, Pinecone).
• Proficiency with multi-GPU and distributed training workflows for efficient scaling of large multimodal models.

Flock encourages individuals who may not meet every qualification to apply. We believe in fostering diversity and welcome applications from candidates who are excited about the role.