Senior Applied Research Scientist - Foundation Models

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Who We Are

Ambient.ai is a unified, AI-powered physical security platform helping the world’s leading enterprises reduce risk, improve operational efficiency, and gain critical insights. Seven of the top 10 U.S. technology companies, along with multiple Fortune 500 organizations, rely on Ambient.ai to modernize their physical security infrastructure.

Who We Are

Ambient.ai is a unified, AI-powered physical security platform helping the world’s leading enterprises reduce risk, improve operational efficiency, and gain critical insights. Seven of the top 10 U.S. technology companies, along with multiple Fortune 500 organizations, rely on Ambient.ai to modernize their physical security infrastructure.

Our platform uses advanced AI and computer vision to seamlessly integrate with existing camera and sensor systems, enabling real-time monitoring and proactive threat detection. By reducing false alarms by over 95%, Ambient.ai allows security teams to focus on real threats and prevent incidents before they occur.

Founded in 2017 and backed by Andreessen Horowitz, Y Combinator, and Allegion Ventures, Ambient.ai is a Series B company on a mission to make every security incident preventable.

We’ve found that in-person time meaningfully supports collaboration, creativity, and team alignment. Our engineering, product, design, and marketing teams work from our Redwood City office 3 days per week. All other Bay Area employees join on Fridays to stay connected and close out the week together.

Ready to learn more? Connect with us on Linked In and You Tube

About

The Role

Ambient.ai is hiring a Senior 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.

What You'll Do

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

• 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.

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

• 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.

• 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.

• 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.
What You'll Bring

• 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)

• Proficient in Python and deep learning frameworks like PyTorch or Tensor Flow. Comfortable with large-scale training pipelines

• Hands-on experience with CNNs, Transformers, and Vision Transformers (ViT). Strong understanding of vision-language models and how to fine-tune or adapt them

• Proven skills in model training and optimization, including fine-tuning on large datasets and applying distillation, quantization, or similar techniques.

Experience with foundation or multimodal models is a plus.

• Strong problem-solving ability: quick prototyping, diagnosing failure cases, and…