Software Engineer, AI Infrastructure - LVM Inference & Evaluation
About the role
Design, build, and optimize AI infrastructure for real-time inference, evaluation, and deployment of computer vision, LLMs, LVMs, and multimodal AI systems at Ambient.ai.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Benefits
- Stock options and the opportunity to share in the company’s growth
- Comprehensive health + welfare package (Medical, Dental, Vision, Life, EAP, Legal Services, 401k plan)
- Flexible time off to rest and recharge, including Winter Break
- The latest tech and swag delivered to your door
- Opportunities to connect with co-workers
- In-person collaboration schedule: Redwood City office three days a week; other Bay Area employees on Fridays
About Ambient.ai
AI company transforming enterprise physical security to prevent…
Ambient.ai is a computer vision intelligence company transforming enterprise security operations to prevent security incidents before they happen. The Ambient.ai platform applies AI and computer vision intelligence to existing sensor and camera infrastructure to deliver continuous physical security monitoring and automate the immediate dispatch of human resources. Ambient.ai is the first platform that goes beyond basic motion detection and image recognition to achieve near-human perception with automated situational context. With physical security incidents on the rise and physical security organizations left under-resourced, Ambient.ai’s computer intelligence platform comes at a time of critical need, which is why the company has raised over $50 million from venture capital investors led by a16z. Today, large enterprises, schools and organizations – including many of the largest US tech companies and other Fortune 500 companies – use Ambient.ai to secure property, people, and assets from the most harrowing physical security threats. The company was founded in 2017 by experts in artificial intelligence from Stanford who previously built iconic products at Apple, Google, Microsoft and Dropbox. We are backed by Andreessen Horowitz (a16z), SV Angel, Y Combinator, and visionary angels like Jyoti Bansal, Mark Leslie and Elad Gil.
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