Lead the advancement of our core embedding-based retrieval systems, focusing on the scientific aspects of modeling.
Fine-tune and extend multimodal models (e.g., CLIP, SigLIP) to improve performance, generalization, and cross-modal alignment.
Work on unifying text and image representations, improving model performance, and ensuring extensibility across evolving product use cases.
Central to Flock’s ability to deliver fast, accurate, and scalable search experiences powered by state-of-the-art vision-language systems.

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.

Flexible PTO: We seriously mean it, plus 11 company holidays.
Fully-paid health benefits plan for employees: including Medical, Dental, and Vision and an HSA match.
Family Leave: All employees receive 12 weeks of 100% paid parental leave. Birthing parents are eligible for an additional 6-8 weeks of physical recovery time.
Fertility & Family Benefits: We have partnered with Maven, a complete digital health benefit for starting and raising a family. In 2025, Flock will provide a $50,000-lifetime maximum benefit related to eligible adoption, surrogacy, or fertility expenses.
Caregiver Support: We have partnered with Cariloop to provide our employees with caregiver support.
Carta Tax Advisor: Employees receive 1:1 sessions with Equity Tax Advisors who can address individual grants, model tax scenarios, and answer general questions.
ERGs: We want all employees to thrive and feel like they belong at Flock. We offer three ERGs today
Women of Flock, Flock Proud, and Melanin Motion. If you are interested in talking to a representative from one of these, please let your recruiter know.
WFH Stipend: $150 per month to cover the costs of working from home.
Productivity Stipend: $300 per year to use on Audible, Calm, Masterclass, Duolingo, Grammarly and so much more.
Home Office Stipend: A one-time $750 to help you create your dream office.

Staff Machine Learning Engineer, Multimodal Modeling

Key skills