Software Engineer, AI Infrastructure - LVM Inference & EvaluationRedwood City · Hybrid

Reporting to Raghu Nallamothu, you will design, build, and optimize the AI infrastructure that powers Ambient.ai http://Ambient.ai’s real-time intelligence platform.

In this role, you will work on the systems required to run state-of-the-art deep learning models across many terabytes of video data in real time. You will help build and scale infrastructure for inference, evaluation, and continuous model improvement across computer vision models, large language models, large vision models, and multimodal AI systems.

This role is ideal for someone with a strong blend of infrastructure engineering, production ML systems, LLM/LVM inference, evaluation harnesses, and inference optimization experience. You will partner closely with research scientists and product engineering teams to bring the latest AI advancements into production for our customers.

• 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.
• 2+ 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 or systems 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, with the ability to work effectively with research scientists, product teams, infrastructure teams, and stakeholders.
• Proactive problem-solving ability, a strong ownership mindset, and adaptability to incorporate new AI technologies and methodologies.
• Experience operating large-scale GPU infrastructure or distributed inference systems.
• Experience with CUDA, NCCL, PyTorch, TensorRT, ONNX, or similar ML 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 techniques.
• Experience with prompt evaluation, model regression testing, human-in-the-loop evaluation, or automated quality gates.
• Familiarity with retrieval-augmented generation, vector databases, embedding models, re-rankers, or search infrastructure.
• Experience building internal ML platforms or tools used by researchers and applied ML teams.

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