Senior Systems Engineer - Autonomous Vehicle Infrastructure jobs at Nvidia

What you'll be doing:

• Develop new Deep Learning models for automatic speech recognition, speech synthesis, neural machine translation and natural language

• Design new large scale training algorithm

• Open-source models using NeMo conversational AI frameworks

• Mentor interns

• Publish research papers on top speech and NLP conferences

• Collaborate with universities and research teams.

What we need to see:

• PhD in Computer Science or Electrical Engineering (or equivalent experience)

• Proven understanding of Deep Learning for Natural Language Processing or Speech Recognition

• At least 5 years of research experience in speech recognition or NLP

• Excellent Python programming skills

• Experience with PyTorch

• Strong publications record

Ways to stand out from the crowd:

• Contribution to open-source projects

• Being reviewers for one of the top speech conferences

What you'll be doing:

• Be part of a cross-business-unit team and own the high-speed IP integration.

• Build a Chiplet floorplan layout design from early assembly/planning through implementation and signoff.

• Work closely with partition owners and Full Chip STA engineers to assure high quality and timely convergence.

• Define and implement efficient, high-quality Chiplet level physical design tools, flows, and methodologies.

• Gain hands-on experience implementing the partition-level BE design (RTL2GDS).

What we need to see:

• Great teammate

• BSEE / MSEE or equivalent experience.

• 8+ years experience in physical design

• Experience in unit and top-level floor planning, bump and RDL layout, full-chip clock tree, power grid planning, and DRC/LVS.

• In depth knowledge of physical design flows and methodologies (PNR, STA, physical verification).

• Deep understanding of all aspects of Physical construction and Integration.

• Knowledge in Physical Design Verification methodology LVS/DRC.

• Familiarity with physical design EDA tools (such as Synopsys, Cadence, etc.)

What you'll be doing:

• Lead, build, and drive the architecture and engineering alignment for key automotive customer projects through all phases, from bringup to production and post-production, using the DRIVE platform.

• Architect and build a seamless integration environment to amplify the scalability of our software solutions for our partners.

• Collaborate with senior leaders across the company to evolve the product initiatives, roadmaps, and processes. Drive innovation bringing in new technologies.

• Lead bring-up activities and provide deep technical guidance and strategies to resolve functional and system performance issues, working with internal and external partner teams.

• Collaborate with our global engineering teams in our US, APAC, and Europe locations to deploy the solution to our customers.

What we need to see:

• BE/BS or MS in computer science, robotics, computer engineering, or equivalent experience.

• Understand the technological evolution in the self driving industry.

• 15+ years of deep hands-on technical experience.

• Extensive strong technical leadership experience across large-scale organizations.

• Established proficiency in application development and scalability for autonomous machines, and familiarity with robotics or automotive related middleware frameworks.

• Broad and deep technical knowledge across software and hardware.

• Proven ability to lead teams across multiple hardware, software and business groups through design and implementation.

• Excellent communication and interpersonal skills and the ability to influence large organizations in meaningful ways.

Ways to stand out from the crowd

• Familiar with automotive design processes and norms (e.g. ISO 26262, ASPICE), including in-vehicle testing, simulation and metrics development of autonomous driving systems.

• Software development experience on QNX or equivalent RTOS.

• Applied knowledge in resolving sophisticated, interrelated issues emanating from sensors to other embedded controllers on the vehicle and from interactions between applications.

• Knowledge of GPU programming such as OpenCL or CUDA and understanding of the NVIDIA DRIVE platform.

• Contributions to or ownership of open-source project and mentorship experience.

NVIDIA is seeking a Sr. Systems Software Engineer for the Apache Spark Acceleration group. Over the past five years GPU accelerated data processing has moved from proof of concept to production deployments. Many enterprises are now recognizing the needs of accelerated computing to handle their large data processing needs. Multi-node GPU deployments will reduce cloud computing costs and lower latency batch ETL workloads.

At NVIDIA, we have been invested in accelerating Apache Spark, providing an open source plugin for Apache Spark. Apache Spark is the most popular data processing engine in data centers. We strive to accelerate Spark applications on GPUs without any code changes. We are passionate about working on hard problems that have an impact. You will need to have strong programming skills, a deep understanding of software development related to C++. You will work with a team that is using open source libraries like RAPIDS to accelerate reading, writing and batch data operations in Spark.

What you'll be doing:

• Develop CUDA/C++ libraries to accelerate DataFrames and I/O operations on common file formats such as Parquet, ORC and JSON

• Collaborate with distributed systems teams to craft solutions to distributed processing problems challenges at large scale

• Work with open source communities to enhance libraries like RAPIDS, CCCL and UCX through technical discussion and code contributions

• Provide recommendations and feedback to teams regarding decisions surrounding topics such as infrastructure, continuous integration and testing strategy

• Build, test and optimize CUDA/C++ libraries across different platforms

What we need to see:

• BS, MS, or PhD in Computer Science, Computer Engineering, or closely related field (or equivalent experience)

• 12+ years of work experience in software development

• Outstanding technical skills in designing and implementing high-quality distributed systems

• Excellent programming skills in C++, Java, and/or Scala

• Ability to work with teams across organizational boundaries and geographies

• Highly motivated with strong interpersonal skills

• OS kernel dev experience is a strong plus

What you'll be doing:

• Use AI to solve product challenges in gaming and other interactive experiences.

• Build upon the latest research to create world-class conversational pipelines for AI assistants and agents.

• Improve and fine-tune language models and retrieval-augmented generation solutions for accuracy and performance.

• Build prototypes to demonstrate real-life applications of your ideas and to accelerate productization.

• Collaborate with NVIDIA's internal and external teams, including AI/DL researchers, hardware architects, and software engineers.

• Participate in technology transfers to and from teams across NVIDIA.

What we need to see:

• PhD or Master’s degree in Computer Science/Engineering, Machine Learning, AI, or related fields; or equivalent experience.

• 12+ years of work experience with last 5+ years focused on language models, AI assistants, and agents.

• Proficiency in C, C++, and Python, with the ability to write high-performance production code.

• Experience with GPU programming, CUDA, and system optimizations is a significant plus.

• A track record of proven research excellence, demonstrated through presentations, demos, or publications at leading venues such as GDC, ICCV/ECCV, SIGGRAPH, or other research artifacts such as software projects or significant product development.

AI-powered machines can learn, reason, and interact with people, thanks to GPU deep learning. We offer competitive salaries and great benefits as a top tech employer with leading talent.

What you’ll be doing:

• Perception experts with application focus will be on multi-sensor fusion based deep learning model development for obstacle perception/fusion in complex driving environments.

• Applied research and development of innovative deep learning and multi-sensor fusion algorithms to improve output accuracy of 3D obstacle perception solutions under challenging and diverse scenarios, with a focus on high-resolution world reconstruction (e.g., occupancy networks).

• Identify and analyze the strength and weakness of the developed 3D obstacle perception solutions using large scale benchmark data (both real and synthetic) and improve them iteratively through KPI building and optimization. This includes careful data verification, model architecture design, understanding details of loss function engineering, and being capable of finding detailed ML bugs and iterating toward perfection.

• Productize the developed 3D obstacle perception solutions by meeting product requirements for safety, latency, and SW robustness, with a strong emphasis on production deep learning model development.

• Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, so that value of data is maximized.

What we need to see:

• 10+ years of hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., PyTorch).

• Experience in multi-sensor fusion (cameras, ultrasonic sensors, radar) for perception tasks, particularly in high-resolution world reconstruction.

• Proven experience in production deep learning model development, including careful data verification, model architecture design, loss function engineering, and debugging ML models.

• Experience in data-driven development and collaboration with data and ground truth teams.

• Strong programming skills in python and/or C++.

• Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other.

• BS/MS/PhD in CS, EE, sciences or related fields (or equivalent experience)

Ways to stand out from the crowd:

• Experience on end-to-end deep learning model development is a plus.

• Proven expertise in developing perception solutions for autonomous driving or robotics using deep learning with multi-sensor input.

• Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications.

• Good understanding of fundamentals of 3D computer vision, camera calibrations including intrinsic and extrinsic, and sensor fusion principles.

• Experience with development in CUDA language. The ability to implement CUDA kernels as part of training or inference pipelines.