Top Tech Jobs & Startup Jobs in Boston, MA

You will integrate communication libraries into AI frameworks, analyze workloads, improve AI compilers, and author custom kernels for performance.

Architect and maintain profiling frameworks for CPU and GPU environments, benchmark EDA applications, analyze memory and kernel performance, identify bottlenecks, optimize memory usage, and develop predictive models for hardware procurement and cloud instance selection.

Conduct research on vulnerabilities and risks in AI systems, develop hardware, software, and algorithmic protections for trustworthy AI, and collaborate across research, hardware, software, and product teams. Publish original research and present findings at conferences. The role requires deep expertise in a security area such as applied cryptography, programming languages, software systems, computer architecture, or AI/ML, along with experimental research and strong coding skills.

The role involves defining verification strategies, building infrastructure for SoC verification, and collaborating with various teams to meet project requirements.

Build and maintain large-scale data pipelines and analytics platforms for EDA datacenter observability, telemetry, reliability analysis, and capacity forecasting. Develop batch and streaming workflows, data models, quality frameworks, monitoring, and retention strategies using metrics, logs, traces, and hardware health data. Partner with observability engineers, data scientists, analysts, and infrastructure teams to deliver scalable, reliable, discoverable datasets for analytical and predictive use cases.

Provide first-line support and troubleshooting for an HPC environment, monitor system health, triage incidents, perform maintenance and patching, and produce runbooks and documentation. Improve processes and escalate to SMEs for complex issues while supporting scheduling, compute, storage, and access concerns.

As a GPU Verification Engineer, you will verify ASIC designs of NVIDIA GPUs, define verification scope, develop test plans, and collaborate with teams.

Designs hardware features for future GPU and parallel-processing architectures, develops graphics and parallel algorithms, and builds simulators, testing infrastructure, metrics systems, and validation tools. Collaborates with architects and researchers across RTL, software models, and real silicon. Requires advanced computer, electrical, or computer engineering education, 8+ years of GPU or CPU architecture experience, strong C, C++, Perl, and Python skills, and expertise in computer architecture, graphics, parallel processing, signal processing, or high-performance computing.

Partner with architecture and design teams to plan and execute functional and performance verification for GPU memory subsystem IP. Develop testbenches, BFMs, monitors and regressions, close coverage, enable FPGA/software testing, and support post-silicon verification and debug to sign off RTL.

Provide technical support to NVIDIA Enterprise customers by triaging and resolving issues, working with engineering on reproductions/logs, applying AI/ML and agentic AI to build support tools and products, documenting interactions and knowledge, and owning cases to resolution (occasional weekend/holiday support).