A Note on applying to Pangram:
At Pangram, we firmly believe that genuine human expression, and our ability to communicate with each other online and through the written word, is a fundamental right- and a value worth preserving.
Please don't use AI to answer any of the questions as part of your application - we want to hear from you!
About Pangram Labs
Pangram Labs’ mission is to protect authenticity and preserve human connection in the context of the generative AI era. We build 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 provide tools for individuals, institutions and companies to detect the use of AI in written content.
Pangram is a recognized thought leader in the space of AI detection. Our CEO & Co-founder Max Spero has been named as one of Time Magazine’s 100 Most Influential People in AI and Pangram has been named one of Time’s Best Inventions of 2026.
We are looking for high agency, mission-driven, and passionate builders to join our team!
About the Role
As a Senior Software Engineer (Backend) at Pangram, you will design, build, and scale the production systems that serve our core technology to a rapidly growing user base and 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.
This is a full-time, on-site role in our Brooklyn office reporting to our CTO.
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
Qualifications:
You are a great fit for this role if you have:
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
You are an exceptional fit for this role if you also 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
Skills Required
- 5+ years of professional software engineering experience with a strong backend focus
- Proficiency in Python and Django or comparable Python web frameworks
- Understanding of RESTful API design, relational databases, message queues, caching strategies, and asynchronous task processing
- Hands-on experience with AWS services such as EC2, ECS/EKS, RDS, S3, SQS, CloudFront, and Lambda
- Experience designing systems for high availability, horizontal scalability, and graceful degradation
- Strong debugging and performance-profiling skills in production environments
- Experience with Docker and Kubernetes
- Familiarity with CI/CD pipelines and infrastructure-as-code tools such as Terraform or CloudFormation
- Experience with monitoring and observability tools such as Datadog, Prometheus, Grafana, or ELK
- Experience deploying and serving deep learning models in production
- Familiarity with React or frontend technologies
What We Do
Pangram's mission is to promote authenticity and preserve human connection in the context of the generative AI era. We firmly believe that genuine human expression, and our ability to communicate with each other online and through the written word, is a fundamental right- and a value worth preserving. At the core, Pangram is best-in-class AI detection software that can be applied to multiple use cases across many verticals. Pangram's core value proposition is its accuracy: a testament to the depth of the research that we have conducted that has been validated by multiple academic third party studies. AI detectors have been widely maligned by the media and the educational community, and we believe somewhat rightfully so: early AI detectors like GPTZero, Copyleaks, ZeroGPT, Turnitin, and had false positive rates of upwards of 1% (false accusations of human writing as AI), and false negatives (missing AI generated text, predicting it as human). They faced many of the issues that still plague AI detection discourse today, such as predicting that the Declaration of Independence is AI-generated and demonstrating elevated false positive rates on ESL writing. We are fighting to earn the trust of the public back by doing AI detection the right way. Public research is core to our identity. We are transparent about what we do, and put our methods out into the open because we have nothing to hide, and we are serious about building trust with our customers. We still believe we are the only AI detector that "actually works," and the only serious team that is up for solving this challenging research problem.
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