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From Home Nvidia Robotics Jobs in Georgia (NOW HIRING)

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From Home Nvidia Robotics information

What is a from home Nvidia Robotics job?

'From Home Nvidia Robotics' jobs refer to remote positions with Nvidia that focus on robotics development, research, or support. These roles may include software engineering, AI development, simulation, or technical support for Nvidia's robotics platforms and tools, such as Isaac Sim or Jetson. Employees in these roles can work from their homes while collaborating virtually with global teams to develop cutting-edge robotics solutions. The work often involves coding, testing, and deploying robotics applications or supporting customers and partners using Nvidia's robotics technologies.

What are the key skills and qualifications needed to thrive as a from home Nvidia Robotics engineer?

To thrive as a Work-From-Home NVIDIA Robotics Engineer, you generally need a background in robotics, computer science, or engineering, with strong programming skills in languages like Python or C++. Familiarity with NVIDIA hardware (such as Jetson platforms), robotics middleware (like ROS), and deep learning frameworks is often required, along with relevant certifications or project experience. Excellent problem-solving, remote collaboration, and communication skills set standout candidates apart. These skills ensure the ability to develop, test, and deploy advanced robotics solutions efficiently while working remotely.

What are the typical collaboration tools and communication methods used by Nvidia Robotics teams working from home?

Nvidia Robotics professionals working remotely typically rely on a suite of digital collaboration tools to stay connected and productive. Common platforms include Slack or Microsoft Teams for instant messaging, Zoom for video meetings, and project management tools like Jira or Asana to track tasks and progress. Code collaboration and version control are usually managed through GitHub or GitLab. Regular virtual stand-ups and documentation sharing ensure everyone stays aligned, and there's a strong emphasis on clear, timely communication to support teamwork across different time zones and disciplines.

What is the difference between From Home Nvidia Robotics vs From Home Nvidia Data Analyst?

AspectFrom Home Nvidia RoboticsFrom Home Nvidia Data Analyst
Required CredentialsBachelor's in Robotics, Computer Science, or related field; certifications in robotics or automationBachelor's in Data Science, Statistics, or related; certifications in data analysis or visualization
Work EnvironmentRemote, involving software development, robotics programming, and simulationRemote, focusing on data collection, analysis, and reporting
Industry UsageRobotics, automation, AI developmentData-driven decision making, analytics, AI training

While both roles are remote and involve working with Nvidia technologies, From Home Nvidia Robotics focuses on developing and programming robotic systems, whereas From Home Nvidia Data Analyst centers on analyzing data to support business and AI insights. The credentials overlap in technical skills, but the job functions differ significantly in their focus areas.

What are the most commonly searched types of Nvidia Robotics jobs in Georgia?

The most popular types of Nvidia Robotics jobs in Georgia are:

What are popular job titles related to From Home Nvidia Robotics jobs in Georgia?

For From Home Nvidia Robotics jobs in Georgia, the most frequently searched job titles are:

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The top searched job categories for From Home Nvidia Robotics jobs in Georgia are:

What cities in Georgia are hiring for From Home Nvidia Robotics jobs?

Cities in Georgia with the most From Home Nvidia Robotics job openings:

From Home Nvidia Robotics jobs near you

Senior Software Engineer, Simulation

Atlanta, GA • On-site

AeroVect Technologies Inc.
Aviation • 1 - 10 employees
Learn more about AeroVect Technologies Inc.external

$125 - $150/hr

Other

Posted 29 days ago

Job description

Who We Are

AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.

You will own and extend the simulation tooling. A significant part of the role is test quality. A test suite that produces flaky failures does not provide usable information. Determinism, reproducibility, and verifying that each gate is capable of failing are ongoing responsibilities rather than one-off tasks.

You Will

  • Build and extend the capabilities of the simulation environment: integration with the production autonomy stack, the interfaces it consumes, vehicle and actor models, and the range of environmental and operational conditions that can be represented

  • Maintain and improve that integration as the autonomy stack evolves, including fidelity work where the difference between simulated and real inputs changes how the stack behaves

  • Build and operate scenario execution in the cloud: orchestration, parallelism, result aggregation, artifact capture, and runtime cost modelling

  • Build and extend the evaluation layer that turns a run into a verdict — assertions and pass/fail criteria precise enough to gate a release and stable enough to avoid false failures

  • Build failure-triage tooling: failure clustering, per-failure recordings, and dashboards that autonomy engineers can use without assistance

  • Extend the scenario authoring tooling used by the V&V team, across backend and frontend

  • Maintain simulation foundations: determinism and reproducibility, pipeline performance, and extending coverage to additional maps and sites

You Have

  • Bachelor’s in Computer Science, Electrical Engineering, Robotics, or related field

  • Strong Python, with a track record of maintainable code in a shared codebase

  • Hands-on simulation experience for autonomous systems, familiarity with simulation platforms such as CARLA, Applied Intuition, Foretellix, NVIDIA Omniverse, IsaacSim, Gazebo, or a proprietary in-house simulatorWorking knowledge of simulation, modelling, and validation methodology, including how simulated results are used to support claims about real-world behaviour

  • Docker and Linux, distributed-systems fundamentals, and experience with GPU-based simulation environmentsCI/CD and cloud execution, experience integrating automated test workflows into CI for end-to-end validation coverage

  • Experience analyzing simulation output and telemetry to identify performance bottlenecks and failure modes

  • Debugging and profiling skills suited to distributed, GPU-bound systems

We Prefer

  • Master’s in Computer Science, Robotics, or related field

  • Experience designing and validating safety-critical systems in autonomous driving, aerospace, or robotics

  • Experience developing or maintaining autonomous vehicle software stacks (ROS/ROS2)

  • Cloud-based simulation infrastructure and large-scale distributed test execution

  • Sensor modelling (camera, lidar, radar), environment generation, or perception ground-truth pipelines

  • Automated testing, continuous integration, and data-driven validation

  • Log/bag re-simulation from recorded real-world data

  • Test-signal quality work: flake reduction, determinism debugging, golden-output comparison

  • Safety standards exposure: ISO 26262, ISO 21448 (SOTIF), UL4600, ISO 13849

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