Top Tech Jobs & Startup Jobs

The Senior Localization Engineer will build robust localization solutions using navigation maps for autonomous vehicles, ensuring real-time performance and adherence to automotive safety standards.

Build and productize agentic AI systems for GPU software development. Responsibilities include developing LLM-powered pipelines for code generation, bug triage, regression detection, and test analysis; creating MCP-based integrations with developer systems; owning agent lifecycle and evaluation; measuring quality metrics; and partnering with GPU kernel engineers to deploy reliable automation across software teams.

Develop tools and libraries for modeling and simulating chip architectures. Support architecture teams in building efficient hardware models using advanced environments and programming skills, particularly in SystemC and C++. Document and validate models with precision.

Lead end-to-end massive-scale data programs for NVIDIA’s Nemotron AI models, from acquisition and curation through release. Translate ML research needs into sourcing plans, establish data quality and compliance standards, manage inventory and IP clearance, and oversee annotation campaigns. Maintain roadmaps, track data velocity, manage risks, and align engineering, legal, operations, vendors, and ML researchers. The role emphasizes generative AI datasets, synthetic data, human-in-the-loop workflows, and evolving multi-stakeholder requirements.

Architects and delivers enterprise integration and API platforms using event-driven, asynchronous, cloud, and AI-augmented workflows. Develops reusable frameworks across SAP, SaaS, and partner ecosystems; governs APIs; integrates EDI, REST, GraphQL, and SOAP systems; and builds autonomous, self-monitoring and self-healing workflows. The role also requires programming, identity and access control expertise, CI/CD automation, observability, and close collaboration with Product and Engineering teams.

Designs and implements verified, high-performance, area- and power-efficient RTL for NVIDIA SoCs and GPUs. Responsibilities include architecture and microarchitecture development, RTL design and synthesis, logic and timing verification, formal verification properties and assumptions, and collaboration across ASIC development teams. The role also involves testbench development, simulation, functional coverage, assertion-based verification, debugging, and coordination with onsite and remote engineering teams.

Designs and models GPU and SoC architectures, develops simulation and analysis tools, validates functional and performance models, creates testing infrastructure, analyzes code coverage, evaluates modeling frameworks, and contributes to AI-based engineering tools. The role also involves cross-team collaboration, documentation, verification, and mentoring junior engineers.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Leads technical developer-relations strategy for NVIDIA platforms across capital markets. Advises quants, traders, developers, financial institutions, ISVs, and cloud providers; demonstrates CUDA, CUDA-X, AI frameworks, and accelerated computing solutions; supports partner onboarding and integrations; develops benchmarks, sample code, and reference implementations; gathers ecosystem feedback; influences product roadmaps; and represents NVIDIA at industry events, workshops, and conferences.

Verify fabric and interconnect IP and full-chip SoC integrations using SystemVerilog/UVM testbenches. Develop constrained-random and directed tests, protocol compliance checks, performance analysis, and collaborate with architecture, design, emulation, and post-silicon teams to ensure correct, high-performance interconnects.