Top Tech Jobs & Startup Jobs

Develop and productize autonomous-driving perception software. Responsibilities include full SDLC execution, writing and testing C++ and Python code, integrating perception components, creating verification plans, deploying deep-learning models to embedded platforms, debugging systems, and evaluating performance with real and synthetic data.

Design and develop real-time online mapping algorithms for autonomous vehicles, generating 3D world models and enriched BEV representations from sensor and external data. Build efficient, safety-compliant embedded C++ software and CUDA-accelerated solutions for NVIDIA’s autonomous driving platform. Responsibilities include sensor fusion, computer vision, state estimation, algorithm integration, runtime optimization, and collaboration across engineering teams.

Supports OEM and strategic customers through sales forecasting, account operations, supply and demand optimization, revenue reporting, fulfillment metrics, pricing, rebates, and lifecycle management. Partners with sales, finance, operations, credit, and product teams to improve processes, manage customer commitments, coordinate product introductions and end-of-life transitions, and develop standardized reporting and analytics.

Develop and productize machine learning prediction systems for autonomous driving. Responsibilities include designing end-to-end prediction solutions, researching deep learning models, supporting diverse driving environments, prioritizing data collection and labeling, using simulation and augmentation, and meeting safety, latency, and software robustness requirements. The role also involves collaboration with data teams and deployment of real-time ML solutions to embedded platforms.

Architects next-generation GPU and CPU systems, developing microarchitectural solutions for connectivity, coherency, power, security, memory, and protocols. Models performance and power, studies AI workloads, collaborates with software and firmware teams, supports implementation, and helps customers optimize hardware performance. Requires advanced computer or electrical engineering expertise, substantial industry experience, strong coding and algorithmic skills, and understanding of LLMs and chip architecture.

Design and optimize multi-agent AI systems for NVIDIA’s CUDA ecosystem, including agent runtimes, compiler-integrated orchestration, evaluation frameworks, and GPU-centric performance solutions. Collaborate across hardware, software, algorithms, compilers, kernels, frameworks, distributed training, and inference teams to accelerate agent planning, tool use, code generation, and other AI workloads.

Develop AI-driven power analysis and optimization methods for NVIDIA GPUs and SoCs. Analyze RTL and gate-level power, build machine learning, reinforcement learning, and LLM-based models, automate power-analysis workflows, identify anomalies and root causes, and recommend architectural and microarchitectural improvements. Collaborate with architecture, ASIC design, physical design, performance, and software teams while prototyping power-conscious features in Verilog.

Define and improve hardware and software architectures for NVIDIA data processing platforms. The role focuses on hardware-software co-design, full-stack performance optimization, benchmarking, workload analysis, system trade-offs, and collaboration with customers, partners, architects, and engineering teams to guide scalable accelerated computing solutions.

Lead technical program management for NVIDIA GPU software, including kernel components, drivers, and firmware. Align chipset and product roadmaps with software capacity, coordinate cross-functional dependencies, make prioritization decisions, partner with hardware and customers, improve planning systems, and track execution metrics. The role requires extensive software product delivery experience and supports process improvements across graphics, machine learning, and accelerated computing programs.

Architects and builds scalable agentic AI systems spanning runtimes, inference, evaluation, orchestration, distributed execution, ETL, HPC, cloud, Kubernetes, and GPU environments. The role optimizes reliability, performance, scalability, resource utilization, and developer experience; prototypes emerging technologies; writes production software; and provides technical leadership across research, engineering, product, and infrastructure teams.