What you'll do
- Design, build, and maintain cutting-edge AI infrastructure for real-time computer vision, LLM, LVM, and multimodal inference workloads.
- Build scalable systems for running state-of-the-art models across large volumes of video and sensor data.
- Optimize inference performance across latency, throughput, GPU utilization, reliability, and cost.
- Develop robust evaluation harnesses and benchmarking systems to measure model quality, system performance, regressions, and production readiness.
- Build infrastructure for continuous model evaluation, experimentation, and deployment.
- Partner with research scientists to productionize the latest advances in computer vision, LLMs, LVMs, RAG, and multimodal AI.
- Improve model-serving architecture, including batching, caching, routing, quantization, model parallelism, and hardware utilization.
- Develop data engines and feedback loops for collecting training data, evaluating model behavior, and continuously improving AI performance.
- Create reliable observability, monitoring, and debugging tools for production AI systems.
- Help define best practices for deploying, evaluating, and operating AI systems in real-world enterprise environments.
What success looks like
You will be successful in this role if you can build practical, scalable infrastructure that helps Ambient.ai http://Ambient.ai deploy better AI models faster and more reliably. You should be comfortable working across the full stack of production AI systems, from model behavior and evaluation to serving architecture, GPU performance, observability, and customer-facing reliability.
This is a hands-on engineering role for someone excited to help bring the next generation of AI, computer vision, LLMs, and LVMs into real-world production environments.