AI Infrastructure Engineer (AI)

Location: Hybrid in Redwood City, United States; engineering teams work from the office three days per week.

Salary: $168,000–$205,000 per year, plus equity.

Company

hirify.global develops AI-powered physical security solutions using computer vision, vision-language models, large language models, and multimodal systems.

What you will do

• Design, build, and maintain infrastructure for real-time computer vision, LLM, LVM, and multimodal inference.
• Build scalable systems for processing large volumes of video and sensor data.
• Optimize inference latency, throughput, GPU utilization, reliability, and cost.
• Develop evaluation harnesses, benchmarks, regression tests, and continuous model-improvement systems.
• Improve model-serving architecture through batching, caching, routing, quantization, parallelism, and hardware optimization.
• Build data engines, observability tools, monitoring, and feedback loops for production AI systems.

Requirements

• 2+ years of industry experience in infrastructure engineering, distributed systems, machine learning platforms, or production AI systems.
• Strong Python programming and software engineering fundamentals.
• Experience building scalable machine learning infrastructure for training, inference, evaluation, and deployment.
• Hands-on experience running deep learning models in production, including LLMs, LVMs, vision-language models, or multimodal models.
• Experience with model-serving systems such as vLLM, Triton Inference Server, or similar technologies.
• Experience with cloud infrastructure, containers, orchestration, distributed systems, and GPU-based workloads.

Nice to have

• Experience with CUDA, NCCL, PyTorch, TensorRT, ONNX, or similar machine learning systems technologies.
• Experience with video understanding, real-time computer vision, multimodal AI, or physical-world AI systems.
• Experience with model compression, speculative decoding, distillation, pruning, or low-latency serving.
• Experience with RAG, vector databases, embedding models, re-rankers, or search infrastructure.
• Experience building internal ML platforms for researchers and applied ML teams.

Culture & Benefits

• Full-time employees receive stock options.
• Medical, dental, vision, life, EAP, legal services, and 401(k) benefits.
• Flexible time off and a winter break between Christmas and New Year’s for most roles.
• Regular opportunities for in-person collaboration and connection with colleagues.
• Work includes the latest technology and equipment delivered to employees.