Senior Machine Learning Engineer, Censys

• We’re a highly collaborative, creative team dedicated to empowering customers with innovative solutions to improve their security posture
• Bachelor’s degree in Computer Science, Data Science, Engineering, or other technical discipline (or equivalent professional experience)
• 3+ years of experience in docker, kubernetes and helm
• Strong proficiency in python and machine learning libraries like PyTorch, Transformers, and Timm
• Proficiency in MLOps tooling like Metaflow, MLflow, Argo Workflows, torchrun and Ray
• Experience working with cloud platforms like AWS, GCP, and Azure

Desirable

• Censys is looking for a Senior Machine Learning Engineer to join our team and help us derive valuable insights from internet security datasets
• This role is responsible for building and maintaining a robust internal Machine Learning Operations (MLOps) platform capable of handling petabyte-scale data and delivering high-throughput, low-latency predictions
• You will engineer and optimize this platform to support a diverse range of machine learning applications, including computer vision, natural language processing, and reinforcement learning, ensuring seamless deployment and scalability
• You’ll work closely with data scientists, engineers, and product teams to build scalable systems that enhance how customers interact with and understand our data. You will design machine learning systems that not only process and analyze data but also bring critical insights to the surface in a way that empowers decision-making
• Deploy and maintain containerized workloads to support machine learning development, deployment, and post-deployment monitoring
• Utilize tools like helm and kustomize to accelerate the deployment of machine learning models and data pipelines
• Apply various optimization techniques such as compilation, quantization-aware-training (QAT), and pruning to improve latency and throughput of models
• Utilize open-source software like Metaflow, Prefect, Temporal, and Argo Workflows to facilitate data science development
• Build and optimize machine learning models to analyze security data, extract actionable insights, and identify trends, anomalies, and other relevant security signals
• Develop and maintain systems for drift detection and model monitoring to ensure continuous improvement and accuracy of insights
• Collaborate with cross-functional teams to design data pipelines that can efficiently process petabytes of raw internet security data
• Telepathic communication