Staff Machine Learning Engineer, Flock

• 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
• 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

Desirable

• As a Staff Machine Learning Scientist 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