Senior Software Engineer, AI Infra

Ambient.ai
Redwood City Posted 7 April 2026

Job Description

Senior Software Engineer, AI Infra Build a safer world with us, one incident at a time. Ambient.ai is the leading agentic physical security platform built to prevent incidents and modernize physical security operations. Our products help 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 protect their people, places and assets. Our platform uses state-of-the-art AI 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 talent, engineering, product, design, and marketing teams work from our Redwood City office three days a week. All other Bay Area employees join on Fridays to stay connected and close out the week together. Recently, we've ranked #71 out of 500 on the Forbes best start up employers list https://www.forbes.com/lists/americas-best-startup-employers/! Ready to learn more? Connect with us on LinkedIn https://www.linkedin.com/company/ambient-ai/?viewAsMember=true and YouTube https://www.youtube.com/@ambient2599 About the role: Reporting to Raghu Nallamothu https://www.linkedin.com/in/raghu-nallamothu-66704724/, you will design and build machine learning infrastructure to run state-of-the-art deep-learning models on many terabytes of video in real time. You will help build a data engine to collect training data and continuously train and evaluate Ambient.ai's computer vision models. Your work will be at the forefront of new developments in AI and computer vision. You will partner with our research scientists to deliver the latest AI advancements, including LLM and LVMs (large vision models), to our product and customers. What you'll do: - You will be playing a pivotal role in building and maintaining cutting-edge machine-learning infrastructure - Develop a robust data engine for collecting training data, ensuring scalability and efficiency in continuous model training and evaluation - Collaborate with research scientists to integrate state-of-the-art AI advancements, including LLMs and LVMs, into the company's computer vision models What you'll bring: - 4+ years of industry experience - BS/MS in Computer Science or related field with a strong programming background - Strong background in machine learning and deep learning. A background in computer vision is a strong plus - Proficient in designing and building scalable machine learning infrastructure, including experience with distributed systems - Expertise in programming languages, especially Python, and sound knowledge of software development best practices - Experience in designing and implementing data engines for collecting and managing training data - Experience with integrating advanced AI models, including LLMs, LVMs, and RAG, into production systems is a strong plus - Strong collaboration and communication skills for effective teamwork with research scientists, product teams, and stakeholders - Proactive problem-solving ability, a mindset for innovation, and adaptability to incorporate new technologies and methodologies Why join us: - We are creating an entirely new category within a 180+ billion-dollar physical security industry and looking for team members who are also passionate about our mission to prevent every security incident possible - We partner with an incredible customer roster of F500 companies, including Adobe, TikTok, Gap and SentinelOne - Regular Full-time employees ... (truncated, view full listing at source)
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