About the job
We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you'll build and scale the critical infrastructure that powers our AI safety systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale.
Responsibilities
Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
Implement automated testing, deployment, and rollback systems for ML models in production safety applications
Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
Contribute to the development of internal tools and frameworks that accelerate safety research and deployment
Qualifications
Minimum
Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
Are results-oriented, with a bias towards reliability and impact in safety-critical systems
Enjoy collaborating with researchers and translating cutting-edge research into production systems
Care deeply about AI safety and the societal impacts of your work
Preferred
Working with large language models and modern transformer architectures
Implementing A/B testing frameworks and experimentation infrastructure for ML systems
Developing monitoring and alerting systems for ML model performance and data drift
Building automated labeling systems and human-in-the-loop workflows
Experience in trust & safety, fraud prevention, or content moderation domains
Knowledge of privacy-preserving ML techniques and compliance requirements
Contributing to open-source ML infrastructure projects