Senior Software Engineer, AI Infrastructure

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 you'll bring

• 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 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.

Nice to Have

• 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.