Senior Software Engineer (Backend)
About Pangram Labs
Pangram Labs’ mission is to protect authenticity by building the world’s best AI detection systems. We publish research on state-of-the-art AI detection techniques and algorithms, and build products to bring our technology into people's daily lives.
We are looking for high agency, mission-driven, and passionate builders to join our team. All roles are in person in Brooklyn, New York.
About the Role
We are looking for a backend-focused Senior Software Engineer to design, build, and scale the production systems that serve our core technology to a rapidly growing user base.
You will own the backend infrastructure that powers Pangram's AI detection platform. This is a senior, high-autonomy role: you'll make architectural decisions, solve hard scaling problems, and ensure our systems are reliable and performant as demand grows. You'll work across our Django backend and AWS infrastructure, collaborating closely with our frontend and machine learning teams.
What You'll Do
Design and implement scalable backend services and APIs to serve AI detection capabilities at scale
Own the reliability, performance, and observability of production systems
Architect and maintain data pipelines, caching layers, and service infrastructure on AWS
Collaborate with ML engineers to deploy and serve machine learning models in production
Identify and resolve bottlenecks across the stack as traffic and complexity increase
Establish and improve engineering best practices around testing, deployment, and incident response
Requirements
5+ years of professional software engineering experience, with a strong backend focus
Proficiency in Python and Django (or comparable Python web frameworks)
Deep understanding of backend architecture patterns: RESTful API design, relational databases, message queues, caching strategies, and asynchronous task processing
Hands-on experience with AWS services (e.g., EC2, ECS/EKS, RDS, S3, SQS, CloudFront, Lambda)
Experience designing systems for high availability, horizontal scalability, and graceful degradation
Strong debugging and performance-profiling skills in production environments
Strongly Preferred
Experience with containerization (Docker) and container orchestration (Kubernetes)
Familiarity with CI/CD pipelines and infrastructure-as-code tools (Terraform, CloudFormation, or similar)
Experience with monitoring and observability tooling (e.g., Datadog, Prometheus, Grafana, ELK)
Nice to Have
Experience deploying and serving deep learning models in production (model serving frameworks, GPU infrastructure, batch vs. real-time inference)
Familiarity with React or frontend technologies sufficient to collaborate effectively across the stack