Senior AI Infrastructure Engineer — Real-Time ML Systems

Ambient.ai; San Francisco (CA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Stock options

Comprehensive benefits

Flexible time off

Job summary

Ambient.ai seeks an experienced AI Infrastructure Engineer to design and scale real‑time AI infrastructure for multimodal models and video data. You will partner with researchers and product teams to deploy cutting-edge ML systems in production.

The role emphasizes LLM/LVM inference, model evaluation, and performance tuning, with a focus on reliability and cost efficiency in an enterprise setting.

Qualifications

• BS/MS in CS or related field or equivalent practical experience.
• 4+ years building infrastructure, distributed systems, ML platforms, or production AI systems.
• Strong Python programming and software engineering fundamentals.

Responsibilities

• Design, build, and maintain AI infrastructure for real-time computer vision, LLM, LVM, and multimodal workloads.
• Scale systems to process terabytes of video and sensor data in real time.
• Optimize inference latency, throughput, GPU utilization, reliability, and cost.
• Develop robust evaluation harnesses and benchmarking for model quality and system performance.
• Build infrastructure for continuous model evaluation, experimentation, and deployment.
• Collaborate with researchers to productionize latest AI advances.

Skills

Python

Machine learning

Distributed systems

GPU utilization

Cloud infrastructure

Model serving

LLM/LVM inference

Observability

Education

BS/MS in Computer Science or related field

Tools

vLLM

Triton Inference Server

CUDA

PyTorch

TensorRT

ONNX

Job description

Ambient.ai seeks an experienced AI Infrastructure Engineer to design and scale real‑time AI infrastructure for multimodal models and video data. You will partner with researchers and product teams to deploy cutting-edge ML systems in production.

The role emphasizes LLM/LVM inference, model evaluation, and performance tuning, with a focus on reliability and cost efficiency in an enterprise setting.