Internship role focused on AI computing software development, specifically either TensorRT-LLM inference optimization or TensorRT Compiler graph optimization to push GPU performance for Generative AI.
Responsibilities
Build and enhance high‑performance LLM inference pipelines on key LLM models.
Analyze and optimize LLM model execution, scalability, and memory use.
Collaborate across framework and research teams to deliver efficient multi‑GPU model serving for Agentic AI workloads.
Work on the TensorRT compiler backend to improve graph transformations and code generation for NVIDIA GPUs.
Own significant product capabilities end-to-end, from ambiguous problem framing through design, implementation, deployment, and iteration with minimal oversight
Locations (4)
Vancouver, British Columbia, Canada
Toronto, Ontario, Canada
Canada
United States
Responsibilities
Own significant product capabilities end-to-end, from ambiguous problem framing through design, implementation, deployment, and iteration with minimal oversight
Build accessible, data-rich user experiences with React and TypeScript that make complex commercial and healthcare insights clear and actionable
Lead architecture and implementation of large-scale OpenStack infrastructure for NVIDIA’s cloud and bare‑metal platforms.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
Responsibilities
Own architecture and lead implementation of large-scale OpenStack infrastructure supporting bare-metal provisioning, virtual machines, and engineering workloads spanning several datacenters around the globe.
Architect and implement Infrastructure as Code frameworks to automate provisioning, configuration, and lifecycle management across OpenStack and bare-metal environments.
Build and improve compute control planes, resource scheduling, topology-aware placement, reservations, and capacity management for heterogeneous hardware.
Develop and extend OpenStack services and integrations, including Nova, Neutron, Ironic, Cinder, Keystone, and Placement.
Improve platform scalability, availability, and performance through architectural changes, profiling, failure analysis, and detailed elimination of operational bottlenecks.
Requirements
BS or MS in Computer Science, Computer Engineering, or a related field, or equivalent experience
This role focuses on scaling the efficiency of our information and protection integrations, ensuring our customers and partners are equipped to build the future of innovative defense.
Compensation
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 implement GPU-accelerated pipelines capable of processing very large (multi-TB and PB sizes) cyber, network, and industry datasets.
Develop proof-of-concept discussions that assist and illustrate how to address accelerated and AI compute workloads to tackle real customer issues.
Function as a technical lead and liaison between NVIDIA engineering, product teams, and OEM partners to drive innovative technical and business strategies.
Guide the development of Agentic AI workflows, including semantic search, retrieval-augmented generation (RAG), and Graph Neural Network (GNN) based applications for threat detection.
Translate complex AI and cybersecurity research into actionable, deployable architectures for enterprise and government customers.
Design, build, and evolve the MDASH scanning engine, integrating AI agents and models to analyze codebases, detect vulnerabilities, and generate fixes.
Locations (3)
Redmond, Washington, United States
San Francisco, California, United States
New York, New York, United States
Compensation
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
Design, build, and evolve the MDASH scanning engine, including agent orchestration, multi-model routing, code understanding, vulnerability detection, reachability validation, and fix generation and verification.
As the senior Site Reliability Engineer you will ensure the reliability, availability, and performance of critical Starbucks technology platforms. You will design and put in place reliability and observability solutions, automate operational work to reduce manual effort, lead incident response and blameless post-incident reviews, and partner with engineering teams to build systems that are resilient, scalable, secure, and ready for peak business demand.
Responsibilities
Ensure Reliability: Design and build for system availability, performance, resilience, and operational readiness across critical platforms.
Engineer for Scale: Automate operations, build and improve observability, and find and remove the root causes of recurring issues and toil.
Champion SRE Practices: Apply and help shape standards for incident management, SLOs, capacity planning, and recovery, and mentor other engineers on SRE best practices.
Requirements
Bachelor’s degree in Computer Science or related field
Nice to have: Hands-on experience building, operating, and troubleshooting reliable distributed systems in production at scale
This job contributes to Starbucks success by delivering high-quality, reliable, and stable technologies and security capabilities in support of the Starbucks Engineering Platform. This position is accountable for the installation, configuration, monitoring, analysis, maintenance, and technical support of the platform.
Responsibilities
Leading and guiding the team to deliver business features
Communicates highly complex ideas and concepts to non-technical peers and customers
Establish cross-functional, collaborative relationships with business and technology partners
Support the development team and perform activities to resolve developer issues in a timely and accurate fashion
Engineer build and release processes
Requirements
Bachelor’s degree in computer science or related field
Nice to have: designing, analyzing and troubleshooting large-scale distributed systems
Develop and enhance analytical performance models and simulation frameworks for AI accelerators, GPU architectures, and large-scale AI systems.
Compensation
Hardware Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Responsibilities
Develop and enhance analytical performance models and simulation frameworks for AI accelerators, GPU architectures, and large-scale AI systems.
Model end-to-end AI training and inference workloads, including compute kernels, attention, MoE, memory access patterns, and communication operations.
Evaluate new architectural features, compute and memory hierarchies, interconnects, and collective communication mechanisms to identify performance and efficiency improvements.
Perform workload mapping and parallelization analysis across tensor, pipeline, expert, data, and context parallelism strategies to optimize accelerator utilization and scalability.
Develop system-level performance models to evaluate throughput, latency, perf/W, and TCO across accelerator, rack, and cluster configurations.
Requirements
Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field
Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field
Lead failure analysis and hardware resiliency for Azure cloud infrastructure, driving detection, prediction, and repair of hardware failures across millions of servers.
Compensation
Hardware Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
Lead failure analysis across server components
Define signals, thresholds, diagnostics and repair recipes
Improve isolation accuracy for repairs
Partner with software engineers and data scientists on detection rules
Leverage AI-powered tools and data-driven insights
Build and maintain real-time computation services (AWS Lambda) that serve scores and calculations on-demand to the customer-facing app, with an eye toward latency, reliability, and cost at scale
Design and maintain reliable, scalable APIs and backend services connecting ODAIA’s data, ML, analytics, and agentic capabilities to customer-facing products
Work across our stack: AWS, Kubernetes, Snowflake, PostgreSQL, and embedded analytical engines like DuckDB and DataFusion
Extend the engine's coverage across vulnerability classes, programming languages, frameworks, build systems, and codebase sizes, from single services to large monorepos.
Integrate program analysis techniques into agent workflows to make detections more precise and grounded.
Improve the engine's precision, recall, consistency, and fix quality, working from evaluation results, research findings, and production feedback.
Optimize scan performance, scalability, cost, and latency so MDASH works well on enterprise-scale codebases and in CI/CD workflows.
Requirements
Bachelor's Degree in Computer Science or related technical field
Master's Degree in Computer Science or related technical field
Nice to have: Master's Degree in Computer Science or related technical field
Nice to have: 8+ years technical engineering experience
Nice to have: 12+ years technical engineering experience
Software EngineerNewOdaia Intelligence Inc. · Remote, Canada+3 more