Lead technical direction for Radiant's cloud storage offering, designing and delivering storage services and data capabilities for demanding cloud and AI workloads.
Responsibilities
Set the technical vision and architecture for Radiant's storage services
Design and build storage systems across object, block, and file
Translate product and customer requirements into robust architectures, APIs, and technical designs
Own performance, durability, availability, and consistency of the storage platform
Profile, benchmark, and tune throughput, latency, and IOPS for AI training and inference
Design and build the AI data layer for a multi-cloud GPU fleet, including data placement, caching, and lifecycle management, while owning reliability and capacity across research teams.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Design and build the AI data layer for a multi-cloud GPU fleet
Build high-throughput data paths for training and RL
Build Kubernetes-native APIs, operators and storage integrations
Own quotas, capacity and data lifecycle across hundreds of research teams
Measure workloads use of data and build I/O telemetry
Requirements
BS or MS in Computer Science, Computer Engineering, Electrical Engineering or a related field, or equivalent experience
Senior Linux Infrastructure Engineer responsible for designing, building, and managing scalable infrastructure platforms across bare metal, hypervisors, containers, and public cloud environments.
Responsibilities
Manage systems at scale through standardization, automation, testing, and monitoring
Define and enforce infrastructure standards for source control, testing, CI/CD, OS, patches, and configuration management
Support a distributed compute environment and multi-petabyte storage platforms
Install, configure, and monitor RHEL-based Linux systems
Troubleshoot hardware and software issues across the full stack
Requirements
Bachelor's degree in Engineering or a related technical discipline
Data scientist with strong background in data mining, machine learning, recommendation systems, and statistics.
Responsibilities
Work with customers to deliver ML/DL projects from beginning to end
Select features, build and optimize classifiers using ML techniques
Data mining using state-of-the-art methods and creating text mining pipelines
Develop machine learning algorithms for object and image classification
Develop NLP models such as transformers and attention models
Requirements
Master’s or PhD Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience
Data scientist with strong background in data mining, machine learning, recommendation systems, and statistics.
Responsibilities
Work with customers to deliver ML/DL projects from beginning to end
Select features and build classifiers using ML techniques
Build and optimize predictive models
Deploy completed models to deliver business impact
Create text mining pipelines to clean and process large unstructured datasets
Requirements
Master’s or PhD Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience
Data Scientist supporting a government contract in Washington, DC, building dashboards, predictive models, and AI/ML solutions.
Compensation
$60,000—$180,000 USD
Responsibilities
Builds, maintains, and improves dashboards, applications, and automations within the Power Platform (PowerBI, Power Automate, and PowerApps), SharePoint, and InfoPath environments to reduce or eliminate manual effort in data processing by automating common processes.
Connects to and leverages application programming interfaces (APIs), system-to-system connections, and manual transfer of data as part of business intelligence extract, transform, and load (ETL).
Builds predictive risk models backed by statistical analysis and supports data-driven solutions.
Identifies and applies usages of AI and ML to extract key insights from unstructured system documentation for analysis, to automate processes, and to integrate with other applications.
Develops and carries out full-service data calls, including creation of collection applications and forms, data normalization, data cleansing, and visualization.
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