Staff Machine Learning Scientist, Flock Safety

• 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

Desirable

• 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 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

• We want our interview process to be a true reflection of our culture: transparent and collaborative. Throughout the interview process, your recruiter will guide you through the next steps and ensure you feel prepared every step of the way
• Our First Chat: During this first conversation, you’ll meet with a recruiter to chat through your background, what you could bring to Flock, what you are looking for in your next role, and who we are
• Engineering Manager Interview: You will meet with Engineering leadership to really dive into the role, the team, expectations, and what success means at Flock
• You should expect the interview will cover your product experience, guiding strategy, stakeholder communication, & launch experience
• This is your chance to really nerd out with someone in your field
• Panel: Learn more about the team, responsibilities, and workflows
• You should be prepared to speak about past projects, how you collaborate and communicate with others, and how you live our values
• Depending on the team and role you are interviewing for, you may meet with several teammates as well as cross-functional partners
• The Executive Review: An opportunity to meet leaders within the ML organization
• They will be looking to get to know you better, understand your motivations, and understand your background
• Be prepared to ask well-thought-out questions about the company, culture, and more