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Temporary Nvidia Autonomous Driving Jobs in Georgia

AeroVect

CA$140K/yr

... and autonomous driving. Our customers include some of the world's largest airlines and ground ... Intuition, Foretellix, NVIDIA Omniverse, IsaacSim, Gazebo, or a proprietary in-house ...

Textron

Autonomy AI/MI Engineer

Augusta, GA · On-site

Own the intelligence of the autonomous system, ensuring that robust perception models enable ... Implement imitation learning (baseline driving) and RL-based policy optimization. * Education:

Textron

Autonomy AI/MI Engineer

Augusta, GA · Remote

Own the intelligence of the autonomous system, ensuring that robust perception models enable ... Implement imitation learning (baseline driving) and RL-based policy optimization. * Education:

Textron

Autonomy AI/MI Engineer

Augusta, GA · Remote

Own the intelligence of the autonomous system, ensuring that robust perception models enable ... Implement imitation learning (baseline driving) and RL-based policy optimization. * Education:

Hot Dip Galvanizing Plant

Be Seen First

Plant Manager - Hot-Dip Galvanizing Operations

Atlanta, GA · On-site

$120K - $170K/yr

Full relocation package & temporary housing allowance available Are you an operational leader with ... Demonstrated success driving performance across safety metrics (TRIR), operational throughput, and ...

Bestzyme

Sr./ Sales Manager, Sweet Protein

Atlanta, GA · Remote

$100K - $130K/yr

... for driving sales and business development efforts in North/South America to achieve sustainable ... High level of autonomy and the ability to work independently; 3. Proven success in building ...

Truist

CSBB Business Execution Senior Analyst

Atlanta, GA · On-site

$89K - $114K/yr

Regular or Temporary: Regular Language Fluency: English (Required) Work Shift: 1st shift (United ... Operating with a high degree of autonomy, the Senior Analyst serves as a reliable execution leader ...

New

Aston Carter

Field Sales Rep [Waycross, GA]

Valdosta, GA · On-site

$26 - $27/hr

If you're energized by meeting new people, managing your own territory, and driving results, this ... This position offers autonomy, strong earning potential, and the opportunity to play a key role in ...

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Temporary Nvidia Autonomous Driving information

What is a temporary Nvidia autonomous driving job?

Temporary Nvidia Autonomous Driving jobs are short-term positions at Nvidia focused on developing, testing, or supporting autonomous vehicle technologies. These roles may involve software engineering, data analysis, system testing, or support functions related to self-driving car platforms. Temporary roles are typically project-based, offering opportunities to work on cutting-edge artificial intelligence and robotics applications within the autonomous driving sector. Such positions provide valuable experience in the fast-evolving field of automated vehicles, even if they are not permanent.

What skills and qualifications are needed for a temporary Nvidia autonomous driving engineer?

To excel as a Temporary Nvidia Autonomous Driving Engineer, you need strong programming skills (especially in C++ and Python), a solid background in robotics or computer vision, and typically a degree in computer science, engineering, or a related field. Experience with autonomous vehicle platforms, Nvidia DRIVE, deep learning frameworks (like TensorFlow or PyTorch), and familiarity with sensor fusion technologies are highly valued. Excellent problem-solving abilities, teamwork, and adaptability are crucial soft skills for integrating new technologies and collaborating across multidisciplinary teams. These competencies ensure safe, innovative solutions in the rapidly evolving field of autonomous vehicles.

What are common challenges faced by a temporary Nvidia autonomous driving specialist, and how can applicants prepare for them?

Temporary specialists in Nvidia's Autonomous Driving division often encounter fast-paced project timelines and rapidly evolving technical requirements. Adapting quickly to new tools, frameworks, and proprietary systems is essential, as is a willingness to collaborate across multidisciplinary teams such as software engineering, data annotation, and hardware integration. Applicants can prepare by demonstrating strong foundational knowledge in machine learning, computer vision, and robotics, as well as effective communication skills to navigate a dynamic, innovative environment.

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

The most popular types of Nvidia Autonomous Driving jobs in Georgia are:

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For Temporary Nvidia Autonomous Driving jobs in Georgia, the most frequently searched job titles are:

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Temporary Nvidia Autonomous Driving jobs near you

Senior Software Engineer, Simulation

On-site, Remote

AeroVect
Aviation • 1 - 10 employees
Learn more about AeroVectexternal

CA$140K/yr

Full-time

Posted 25 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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