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Temporary Nvidia Engineering Jobs (NOW HIRING)

... C++ and Python programming. Additional Skills & Qualifications * Experience with NVIDIA Orin ... If eligible, the benefits available for this temporary role may include the following: * Medical ...

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... NVIDIA, SpaceX, and Skydio. We are building the infrastructure layer for sovereign and edge AI ... Collaborate with designers, product managers, and engineers to ensure copy and design are developed ...

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Temporary Nvidia Engineering information

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$57K

$137K

$197K

How much do temporary nvidia engineering jobs pay per year?

As of Aug 7, 2026, the average yearly pay for temporary nvidia engineering in the United States is $137,006.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,500.00 and $151,500.00 per year, depending on experience, location, and employer.

What is a temporary Nvidia engineering role?

Temporary Nvidia Engineering jobs are short-term positions at Nvidia, typically filled to meet project demands, cover employee absences, or provide specialized skills for a limited period. These roles can range from hardware and software engineering to research and development positions. Temporary engineers work on innovative projects involving graphics processing, AI, and other cutting-edge technologies. Although these jobs are not permanent, they provide valuable experience and networking opportunities within a leading tech company like Nvidia.

What are the key skills and qualifications needed to thrive as a temporary Nvidia engineer, and why are they important?

To thrive as a Temporary Nvidia Engineer, you need a solid background in computer engineering, programming (C/C++ or Python), and experience with hardware or software development, often supported by a relevant degree. Familiarity with Nvidia’s development tools such as CUDA, GPU architecture, and version control systems is typically required. Strong problem-solving skills, adaptability, and effective communication help you quickly integrate with teams and adapt to project needs. These skills ensure high productivity and quality contributions in a fast-paced, innovation-driven environment where contract roles demand rapid impact.

What is the difference between Temporary Nvidia Engineering vs Temporary Nvidia Data Scientist?

AspectTemporary Nvidia EngineeringTemporary Nvidia Data Scientist
Required CredentialsBachelor's or Master's in Engineering, Computer Science, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentHardware development, software engineering, system testingData analysis, model development, statistical analysis
Employer & Industry UsageUsed in hardware and software product development within NvidiaUsed in AI, machine learning projects, and data-driven solutions at Nvidia

Temporary Nvidia Engineering focuses on hardware and software development, requiring engineering credentials and working in product development environments. In contrast, Temporary Nvidia Data Scientist emphasizes data analysis and modeling skills, working primarily on AI and machine learning projects. Both roles are essential in Nvidia's innovation pipeline but differ in their technical focus and daily tasks.

What types of projects do temporary Nvidia engineers typically work on, and how do they collaborate with full-time teams?

Temporary Nvidia Engineering roles often focus on short-term, high-priority projects such as software development, hardware validation, or performance optimization. Contractors are usually integrated into existing teams and work closely with full-time engineers, project managers, and cross-functional partners to meet project milestones. While the assignments are time-bound, temporary engineers are expected to contribute actively during team meetings, participate in code reviews, and share updates regularly. This collaborative environment helps ensure project continuity and gives temporary staff valuable exposure to Nvidia’s cutting-edge technologies and workflows.
More about Temporary Nvidia Engineering jobs
What cities are hiring for Temporary Nvidia Engineering jobs? Cities with the most Temporary Nvidia Engineering job openings:
What are the most commonly searched types of Nvidia Engineering jobs? The most popular types of Nvidia Engineering jobs are:
What states have the most Temporary Nvidia Engineering jobs? States with the most job openings for Temporary Nvidia Engineering jobs include:
Infographic showing various Temporary Nvidia Engineering job openings in the United States as of August 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 50% In-person, 17% Hybrid, and 33% Remote job distribution, with an average salary of $137,006 per year, or $65.9 per hour.

Senior Staff Engineer, Autonomy Integration (R5441) (San Diego)

Shieldai

San Diego, CA • On-site

$230K - $350K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Job Description:

This role is the team's primary interface between perception software and the flight test enterprise. You will work alongside autonomy/perception software developers, flight test engineers, and platform integration teams to mature our capabilities from algorithm to flight-verified system. The ideal candidate brings hands-on experience deploying and testing autonomy or perception software on edge hardware, a strong foundation in one or more core perception disciplines (detection, tracking, SLAM, or navigation), and a bias toward field execution over theoretical analysis.

  • Coordinate software and hardware integration activities that enable the autonomy team to deploy and test their stack across a range of platforms, including small UAS, manned fixed-wing aircraft, and unmanned fixed-wing surrogate platforms.
  • Drive sensor selection for EO/IR and A-PNT perception software testing on operationally relevant or surrogate platforms; support flight test engineers in sensor bring-up, characterization, calibration, and maintenance.
  • Own the fly-fix-fly cycle for autonomy and perception software: integrate the Hivemind Pilot stack onto target hardware, verify correct deployment and configuration, define test objectives for flight test campaigns, help execute tests in the field, and analyze collected data to determine pass/fail status against objectives.
  • Own sensor calibration and time synchronization processes for all A-PNT and perception sensors used in testing, including camera intrinsic/extrinsic calibration and camera-IMU temporal alignment.
  • Establish data quality standards and curate data management processes for field-collected sensor data, ensuring that recordings from flight test campaigns are usable for offline analysis and algorithm development.
  • Resolve technical and logistical ambiguities at the boundary between software development and flight test operations, and make command decisions that keep perception maturation efforts moving forward.
  • Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or 5 years with a PhD; or equivalent work experience.
  • Hands-on experience calibrating and time-aligning EO/IR and position, navigation, and timing (PNT) sensors, including both COTS and military-grade hardware.
  • Demonstrated ability to thrive in fast-paced field testing environments, with comfort operating at the intersection of hardware, software, and flight operations.
  • Experience in design of experiments, flight test card development, and establishing verifiable, measurable test objectives.
  • Experience deploying perception or autonomy software to edge hardware platforms (e.g., NVIDIA Jetson or comparable embedded compute).
  • Proficiency in Python for data analysis and visualization; comfort reading and making targeted modifications to algorithms implemented in C++.
  • Track record of solving problems that span hardware, software, and engineering capacity constraints across functional or organizational boundaries.
  • Demonstrated academic training or professional experience in a core perception discipline such as object detection, object tracking, SLAM, or navigation, preferably in a defense or aerospace context.
  • Experience with camera calibration frameworks (e.g., Kalibr) and camera-IMU extrinsic and temporal calibration workflows.
  • Familiarity with rosbag-based data collection, replay, and analysis pipelines in robotics or autonomy systems.
  • Experience characterizing EO/IR gimbal cameras, including MTF, distortion, and radiometric properties.
  • Background supporting DoD or defense aviation programs, including experience operating under controlled airspace or range environments.
  • Familiarity with Conan-based, Nix-based, or CMake-based build systems and embedded Linux development.
  • Experience with CI/CD pipelines and test automation in the context of embedded software.

This role is hybrid, based at Shield AI's San Diego, CA headquarters (three days per week in office), with periodic travel to flight test sites required.

Staff: 195,000-$290,000

Senior Staff: $230,000- $350,000

Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed toequal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

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