Applied Research Scientist - Foundation Models

Ambient.ai is the category creator and leader in Agentic Physical Security. Powered by Ambient Pulsar, the first reasoning Vision-Language Model purpose-built for physical security, our platform seamlessly integrates with existing security cameras and physical access control systems to unify monitoring, access control, threat assessment, response, and investigations through an always-on reasoning layer that augments security operators with superhuman capabilities. The results: 95% fewer false alarms, investigations 20x faster, and 10x faster response.

The momentum speaks for itself: we doubled new ARR in FY26, we process 200M+ video hours per day, and have delivered results for world-class customers including Cisco, ServiceNow, SentinelOne, TikTok, Bayer, and MoMA. That kind of momentum creates an environment where great people thrive, and it shows: we recently ranked #71 out of 500 on the Forbes best startup employers list.

Founded in 2017 and backed by Andreessen Horowitz, Y Combinator, and Allegion Ventures, Ambient.ai is on a fast-paced journey to fulfill our mission: prevent every security incident possible.

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Ambient.ai is hiring a Applied Research Scientist to build the next generation of foundation models for computer vision. You will join a team responsible for building multimodal models with state-of-the-art performance on real-world vision benchmarks. In this role, you’ll own full-cycle model development: from pre-training and fine-tuning on image-language data to applying distillation and compression techniques for deployment. This is a hands-on, cross-functional role where your work will directly impact our mission of preventing every security incident possible.

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  Develop & Optimize VLMs: Design and optimize transformer-based vision-language models to understand images, videos, and text, and optimize for real-time inference.

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  Pre-training & Fine-tuning: Own the full training pipeline—from pre-training on image-text data to fine-tuning for Ambient.ai’s physical security domain and use cases.

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  Model Compression & Optimization: Apply techniques like distillation, quantization, and pruning to reduce model size and latency, enabling efficient edge deployment.

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  Leverage Open-Source & Innovate: Use and extend state-of-the-art open-source models. Prototype new architectures and training methods to advance Ambient.ai’s multimodal AI research.

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  Cross-Team Collaboration: Work with engineering and product teams to integrate models into the platform. Iterate based on real-world feedback and deployment data to improve performance.

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  Research and Experimentation: Stay current with vision, NLP, and multimodal AI research. Design experiments to test new algorithms and continually enhance our core AI systems.

• Ph.D. or Master’s in CS, EE, or related field, with a strong foundation in AI/ML (Ph.D. preferred or Master’s with strong experience)