Every job here shows a published salary in the $400k+ range · average $382k · updated 5h ago
Own the technical vision, architecture, and roadmap for 3D obstacle perception to support end-to-end autonomous driving functionalities, leveraging state-of-the-art CNN and transformer-based architectures where appropriate.
Own board and system level hardware reliability across NVIDIA's product lines, including Data Center, Server, Graphics Cards, Automotive, Embedded, and emerging platforms.
Modeling and analysis of SoC & Systems algorithms and features. Work in a matrixed environment, across the different modelling teams, to document, design, develop tools to analyze and simulate, validate, and verify models.
Develop Java, Scala and CUDA/C++ libraries to accelerate DataFrames and I/O operations on common file formats such as Parquet, ORC and JSON. Enable interoperability with table formats such as Apache Iceberg and Delta Lake, and metastores such as Unity Catalog.
Develop and maintain deep technical expertise in the application of generative and agentic in digital health.
Lead a multi-year volume and revenue forecast by product line. Focus strongly on the next four rolling quarters. Collaborate closely with Industry Business Development, Regional Sales, Finance, and Product Management teams.
Build and scale data generation pipelines using LLM-based methods combined with automated quality evaluation. resulting in datasets to improve both initial training and fine-tuning of LLMs such as Nemotron.
Act as the lead engineer for developing the agent frameworks natively on Windows environments. You will build the technical roadmap to bring always-on, self-evolving AI assistants to GeForce RTX PCs and laptops.
Ask you to define the multi-generation strategy for application scaling across DRAM, NVLink, C2C, compute, and the supporting software stack.
Architecture: Define the canonical harness architecture and component baseline. Engineering standards: Set the engineering standards the team builds on. Technical roadmap: Guide the Find, Validate, and Patch technical roadmap.
Leading and coaching a team of Storage Production Engineers, creating a collaborative, inclusive, and learning focused environment. Designing, deploying, and improving large scale storage systems, including distributed storage, parallel file systems, and object storage.
Leading the development of next-gen Scale-up fabric for AI data-centers. Concentrating on collaborative communication and vertical co-development of AI networks alongside hardware, software, and applications.
Digital Orchestration & S2P Simplification: Deploy a unified "front door" portal for all S2P requests, build buying authorization frameworks, and implement automated document parsing for PR creation logic.
Partnering with the legal team and executive team on our IP licensing matters. Managing our pre-litigation activities relating to IP licensing. Negotiating licenses.
Various Architectural Work: Architect distributed pipelines, define and drive the technical implementation of high-throughput, low-latency, distributed inference systems to support massive-scale AI workloads.
Build the team and the function. Hire and develop a team of five inventory control specialists spanning Factory, Fulfillment, Logistics, OSAT, and Service categories. Establish the continuous control monitoring system they manage.
Design, implement, and institutionalize the integration readiness process for all next-generation datacenter board products — owning manufacturing and test readiness end to end so build starts land on schedule and at target yield.
Lead the canonical DriveOS process architecture, terminology, roles and interfaces across engineering, program management, quality, functional safety, and cybersecurity.
You will own NVIDIA's RL post-training frameworks strategy: where we invest directly, where we partner upstream, and how we prioritize based on customer impact, ecosystem leverage, technical feasibility, and opportunity cost.
Define and prototype AI system architectures spanning optical and e-beam inspection, wafer and mask inspection, metrology, defect review, equipment signals, and process data.
Manage and mentor a team of Developer Relations Managers to cover AI for science (life sciences, physics, robotics, quantum, climate). Set direction, remove blockers, and build a high-trust, high-performance team.
NVIDIA · 15d ago
Lead and mentor a team of product development engineers responsible for validation, characterization, and production ramp of NVIDIA product lines. Drive product development strategy from early silicon validation through qualification and high-volume manufacturing.
Set the technical vision and architecture for NVIDIA’s next-generation autonomous driving stack, spanning both classical and learning-based approaches.
Lead and grow globally distributed firmware teams and engineering managers. Own delivery from architecture and pre-silicon development through bring-up, production, and long-term support.
$400k+ jobs on Six Figure Jobs: 1,131 roles with a published salary range. Average salary: $382k USD. Salaries come from the employer’s own posting, and every apply button says whether it goes to the company’s site or a job board. Filter by role, country or work type to narrow the list.
