AI Engineer

Company Overview:

Cellebrite’s (Nasdaq: CLBT) mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world. Cellebrite’s AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on Cellebrite’s digital forensic and investigative solutions-available via cloud, on-premises and hybrid deployments-to close faster and safeguard communities.

To learn more, visit us at www.cellebrite.com, https://investors.cellebrite.com/investors and find us on social media @Cellebrite.

Position Overview:

We are looking for a talented and driven developer to join our Research Engineering team and contribute to the development of scalable, cloud-native AI solutions on AWS. This role focuses on building and maintaining infrastructure and services that support advanced machine learning workflows, real-time inference, and robust observability.

You will research and build data-driven applications. Research and develop Machine Learning algorithms to turn forensic data into investigation leads, evidence, and intelligence insights. Be a part of a heterogeneous, multi-disciplinary research team. Take projects end to end, from the inception of the idea, through data and algorithm research and POC development. The work is both analytical and experimental in nature and includes prototyping of new algorithms, investigating data, and evaluating algorithm performance.

Responsibilities



• Design and implement features for our AWS-based AI solution, including job orchestration, manifest generation, and API integrations.
• Develop and maintain CI/CD pipelines using GitHub Actions, CDK, and AWS infrastructure (e.g., SageMaker, Lambda, Bedrock).
• Ensure code quality and reliability through comprehensive unit testing, performance testing, and observability enhancements.
• Collaborate with cross-functional teams to integrate AI services into broader investigation workflows and runtime environments.
• Troubleshoot deployment issues and contribute to the continuous improvement of dashboards and monitoring tools.
  
  

• Proficiency in Python and Typescript, with experience in API development and cloud-native architectures.
• Hands-on experience with AWS services including SageMaker, Lambda, etc.
• Experience with machine learning libraries such as PyTorch, Pandas, NumPy, and related data processing tools.
• Ability to write clean, maintainable code and contribute to shared documentation and best practices.
• Familiarity with GitHub workflows, secrets management, and secure deployment practices.
• Familiarity with Amazon EKS and Kubernetes-based deployments.
• Experience with GenAI infrastructure, semantic caching, and LLM integration.
• Knowledge of observability tools like Datadog and log aggregation strategies.
• Prior contributions to scalable AI platforms or high-throughput batch processing systems.