Who is Flock?

Every community deserves to be safe, it’s a fundamental right. Our mission is simple - to build technology that reduces crime and protects privacy. Flock partners with cities, businesses, schools, and neighborhoods to help protect where people live, work, and play. Last year, Flock technology supported over 1 million criminal investigations, helped solve approximately 20% of reported crimes in areas where it is deployed, and played a role in locating more than 10,000 missing people.

We are a high-performance team united by urgency, ownership, and a shared commitment to meaningful impact. The work is fast-paced and the expectations are high. We push beyond perceived limits, support each other, and hold ourselves accountable to delivering results that matter.

With over $1B in funding and an $8.3B valuation, we are scaling with intention and investing in the people who will help us build what others said could not be done. At Flock, you will find the opportunity to grow quickly, take on real responsibility, and contribute to something bigger than yourself.

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 onboarding to the things that matter to deliver value.

The First 60 Days 

• Gain familiarity and performing R&D

• Begin to automate the systems for training, evaluation, testing, and model release

90 Days & Beyond 

• Own long-term maintenance

• Become a leader for the team offering hand-ons help

• Begin exploratory work

The Interview Process 

We want our interview process to be a tr