AI/ML Engineer

Build and Improve Production AI Models

• Design, train, fine-tune, and deploy state-of-the-art computer vision models for object detection, image understanding, and visual search.
• Improve model robustness across challenging real-world scenarios including varying lighting conditions, perspective changes, motion blur, occlusion, compression artifacts, and partial captures.
• Continuously evaluate and iterate on models using production feedback and newly collected data.

Optimize AI for Mobile and Cloud

• Optimize inference performance for both edge and cloud deployments, balancing accuracy, latency, memory usage, and operational cost.
• Improve mobile ML performance across iOS and Android while accounting for device constraints such as thermal throttling, battery usage, and hardware acceleration.
• Build scalable cloud inference services capable of supporting high-volume production workloads.

Build a World-Class ML Platform

• Design reproducible training pipelines, model versioning strategies, and automated evaluation workflows.
• Establish quality gates and validation processes to ensure models meet production standards before deployment.
• Improve CI/CD pipelines, artifact management, and promotion workflows across development, staging, and production environments.

Own Data Quality and Evaluation

• Develop data collection strategies that improve model performance and generalization.
• Create synthetic and augmented datasets to increase robustness across diverse operating conditions.
• Build automated benchmarking and evaluation pipelines with measurable performance metrics and regression testing.

Improve Visual Search and Retrieval

• Design and optimize image embedding and similarity search pipelines.
• Improve semantic matching, reranking, and retrieval quality for image-based search experiences.
• Evaluate new architectures and techniques that enhance accuracy and user experience.

Collaborate Across Engineering Teams

• Work closely with mobile and backend engineers to integrate AI models into production applications.
• Debug end-to-end ML systems, from training pipelines and inference services to client-side image preprocessing and post-processing.
• Contribute to technical architecture decisions and establish best practices for scalable AI development.

Drive Innovation

• Evaluate emerging AI technologies and identify opportunities to improve existing capabilities.
• Prototype new features in computer vision, multimodal AI, recommendation systems, and conversational AI.
  • Help shape the long-term AI strategy and technical roadmap.