Overview
In this role, you will design, develop, and deploy state-of-the-art
computer vision and language models that power scalable, real-world solutions. You'll work with large-scale image and video data, building and optimizing production-grade vision systems while contributing clean and modular code to shared repositories.
As part of our AI team, you'll collaborate closely with engineering teams to deliver high-impact features for our growing SaaS platform. The ideal candidate brings hands-on experience deploying computer vision and language models in production and applying MLOps best practices on cloud platforms.
Responsibilities
- Fine-tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale.
- Develop vision-language models and Mixture of Experts architectures, from experimental design through production deployment.
- Architect Retrieval-Augmented Generation (RAG) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation.
- Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade computer vision models, with an emphasis on clean, modular, maintainable code.
- Contribute to our machine learning repositories and optimize models for performance, scalability, and real-time inference across edge and cloud environments.
- Drive performance optimization and scalability of ML systems across edge and cloud environments.
- Collaborate with cross-functional teams to integrate computer vision solutions into end-to-end products, translating research outcomes into measurable platform impact.
This list of responsibilities might not cover everything you'll end up doing.
Qualifications
- 5+ years of hands-on machine learning experience, with deep specialization in computer vision and a proven track record of shipping models to production.
- Master's degree required (Ph.D. preferred) in Computer Science, Machine Learning, or a closely related field.
- Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.
- Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) able to own the full model lifecycle from experimentation through production monitoring.
- Experience building and deploying LLM-based systems and Retrieval-Augmented Generation (RAG) pipelines, including vector store integration and retrieval evaluation.
- Strong communicator who can translate complex research findings into actionable decisions for engineering and product stakeholders.
Benefits
We offer a Total Rewards plan designed with you and your family's health and wellness in mind that includes:
- Paid days off (i.e. vacation, sick days, bereavement leave)
- Health and Dental plans
- Retirement plans
- Employee and Family Assistance Program (EFAP)
- Employee referral program
