Applied Scientist, Amazon Optics

Amazon Amazon · Big Tech · Herndon, VA · Applied Science

The Applied Scientist will design and develop ML models for physical security operations, including automated alarm triage, false alarm suppression, and anomaly detection. They will build LLM-based systems for querying and summarizing incident data, develop predictive models for security patterns, and research computer vision applications for threat detection. The role involves architecting ML pipelines, defining evaluation frameworks, and owning model performance in production. They will also drive scientific breakthroughs in multi-modal fusion, few-shot learning, and reinforcement learning, and collaborate with software engineers and cross-functional stakeholders to integrate ML solutions into the Optics platform.

What you'd actually do

  1. Design and develop ML models for automated alarm triage, false alarm suppression, and anomaly detection across physical security signal streams (access control events, intrusion alarms, video analytics triggers)
  2. Build and optimize LLM-based systems that enable security operators to query, summarize, and interact with incident data using natural language
  3. Develop predictive models that identify emerging security patterns, correlate multi-source signals, and enable proactive incident response before threats materialize
  4. Research and implement computer vision applications for video-based threat detection, object classification, and automated situational awareness during active incidents
  5. Architect end-to-end ML pipelines from data ingestion through model training, evaluation, deployment, and monitoring in production

Skills

Required

  • ML models
  • LLM-based systems
  • computer vision
  • ML pipelines
  • evaluation frameworks
  • model performance monitoring
  • multi-modal fusion
  • few-shot learning
  • reinforcement learning

Nice to have

  • physical security
  • signal processing
  • anomaly detection
  • access control
  • intrusion alarms
  • video analytics
  • natural language processing
  • predictive modeling
  • threat detection
  • situational awareness
  • data ingestion
  • model training
  • model evaluation
  • model deployment
  • model monitoring
  • A/B testing
  • drift detection
  • retraining
  • model updates
  • scientific breakthroughs
  • ML best practices
  • model selection
  • feature engineering
  • experimental design
  • product roadmaps
  • customer-facing features
  • mean time to resolution
  • false alarm rates
  • mean time to detection
  • automation coverage
  • internal research
  • broader ML/security community
  • papers
  • patents

What the JD emphasized

  • physical security operations
  • automated threat detection
  • signal processing
  • AI-driven security evaluation
  • LLM-based services
  • physical security incidents
  • automated false alarm resolution
  • intelligent signal processing
  • predictive insights
  • real time
  • multi-modal fusion
  • few-shot learning
  • reinforcement learning

Other signals

  • LLM-based systems
  • ML models
  • computer vision
  • multi-modal fusion
  • few-shot learning
  • reinforcement learning