Senior Software Engineer, AI Infrastructure - LVM Inference & Evaluation

• 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 product ionize 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.

• 4+ years of industry experience building infrastructure, distributed systems, machine learning platforms, or production AI systems.
• BS/MS in Computer Science or a related technical field, or equivalent practical experience.
• Strong programming background, especially in Python, with solid software engineering fundamentals.
• Experience designing and building scalable machine learning infrastructure for training, inference, evaluation, and deployment.
• Hands-on experience running deep learning models in production, ideally including LLMs, LVMs, vision-language models, or multimodal models.
• Strong understanding of inference optimization techniques, including batching, caching, quantization, parallelism, memory optimization, GPU utilization, and latency reduction.
• Experience with model-serving frameworks such as vLLM, Triton Inference Server or similar technologies.
• Experience building evaluation frameworks, test harnesses, benchmarks, regression tests, or model-quality measurement systems.
• Strong background in machine learning and deep learning; computer vision experience is a strong plus.
• Experience designing data engines or pipelines for collecting, managing, and curating training and evaluation data.
• Familiarity with integrating advanced AI systems such as LLMs, LVMs, RAG pipelines, embedding models, or multimodal models into production applications.
• Experience with cloud infrastructure, containers, orchestration, distributed systems, and GPU-based workloads.
• Strong collaboration and communication skills.
• Proactive problem-solving ability, a strong ownership mindset.

Demonstrates expertise in building and optimizing AI infrastructure for real-time computer vision and multimodal inference workloads, with a strong focus on performance optimization and model evaluation. Proficient in deploying scalable machine learning systems and integrating advanced AI models into production environments.

• VLLM
• Triton Inference Server
• Cloud Infrastructure
• Containers
• Orchestration