Job description
About Reality Defender
Reality Defender provides accurate, multimodal AI-generated media detection solutions to enable enterprises and governments to identify and prevent deepfake-driven fraud in real time. The winner of RSA’s 2024 Innovation Sandbox, a Y Combinator graduate, and backed by DCVC, Accenture, IBM, and Booze Allen Hamilton, Reality Defender is the first company to pioneer multimodal and multi-model detection of AI-generated media. Our web app and platform-agnostic API built by our research-forward team ensures that our customers can swiftly and securely mitigate fraud and cybersecurity risks in real time with a frictionless, robust solution.
Youtube: Reality Defender Wins RSA Most Innovative Startup
Reality Defender’s solutions stand out because they are:
Proven: Accurate in the real world and continuously engineered to be resilient
Multimodal: Detects impersonations in any multimedia format
Real Time: Automated alerting of ongoing deepfake attempts
Integrated: Flexible deployment options across existing tech stack and applications
Responsibilities
Architect and manage our core MLOps infrastructure for model training, validation, and high-availability inference serving.
Develop and own our CI/CD/CT (Continuous Integration, Delivery, and Training) pipelines to automate the testing and deployment of ML models.
Implement comprehensive monitoring and alerting for model performance, data drift, and system health to guarantee production stability and uptime.
Implement and maintain security best practices throughout the ML lifecycle, including data privacy, access management, and infrastructure hardening, in close collaboration with security and engineering teams.
Partner closely with the AI and Engineering teams to streamline workflows, remove bottlenecks, and empower them to deliver value faster.
Minimum Qualifications
BS in Computer Science, a related technical field, or equivalent practical experience.
3+ years of professional experience in an MLOps, DevOps, or Software Engineering role with a focus on infrastructure.
Hands-on experience with at least one major cloud provider (e.g., AWS, GCP, Azure).
Strong proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Demonstrated experience designing and implementing automated CI/CD pipelines from scratch (e.g., using Jenkins, GitHub Actions).
Preferred Qualifications
MS in Computer Science or a related technical field.
Proficient in Python, with experience writing scientific software and collaborating in code-centric research environments.
Deep familiarity with AWS and Terraform - codified VPCs, EKS clusters, IAM least-privilege policies, and multi-account landing zones are second nature to you.
Comfortable with ML workflow orchestration and metadata tools such as MLflow or Airflow, and experienced in Linux system administration.
Skilled in configuring monitoring and observability platforms like Weights & Biases or Datadog, with the ability to integrate GPU-level metrics and build real-time dashboards tracking utilization, memory, error rates, drift, and latency across training and inference.
Strong grasp of the end-to-end machine learning lifecycle, from data ingestion and processing through model training, evaluation, deployment, and monitoring.
Experience working with human-centered, complex, and often messy datasets, with domain knowledge in social sciences or adjacent fields such as behavioral research, human-computer interaction, or digital media.
