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Cuda Programming Jobs in Virginia (NOW HIRING)

Booz Allen Hamilton

Computer Vision AI Engineer

Mclean, VA · On-site

$99K - $225K/yr

Utilize GPU programming, including CUDA or RAPIDs, to optimize the performance of computer vision applications. * Contribute to the architecture and implementation of embedded systems programming ...

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$27

$53

$81

How much do cuda programming jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for cuda programming in Virginia is $53.89, according to ZipRecruiter salary data. Most workers in this role earn between $43.61 and $62.93 per hour, depending on experience, location, and employer.

What is the salary of NVIDIA CUDA developer?

The salary of an NVIDIA CUDA developer typically ranges from $80,000 to $130,000 annually, depending on experience, location, and industry. Skilled CUDA programmers with advanced knowledge of parallel computing and GPU architecture tend to earn higher salaries.

What jobs use CUDA?

Jobs that use CUDA include roles such as GPU programmer, software developer, data scientist, and machine learning engineer, especially in fields like high-performance computing, artificial intelligence, and scientific research. These roles often require knowledge of parallel programming, C++, and GPU architecture, and involve developing or optimizing software to run efficiently on NVIDIA GPUs.

Are CUDA programmers in demand?

CUDA programmers are in high demand due to the growing use of GPU computing in fields like artificial intelligence, scientific research, and data processing. Skills in parallel programming, GPU architecture, and CUDA toolkit are highly valued, and job opportunities are expected to increase as these technologies expand across industries.

How much do CUDA engineers make?

CUDA engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in parallel programming and GPU optimization can command higher salaries, especially in tech hubs or companies with advanced AI and high-performance computing needs.

What is the difference between Cuda Programming vs GPU Developer?

AspectCuda ProgrammingGPU Developer
Required CredentialsKnowledge of CUDA, C/C++, parallel computingKnowledge of GPU architecture, CUDA, OpenCL, C/C++
Work EnvironmentHigh-performance computing, scientific research, AIGraphics, gaming, scientific visualization, AI
Industry UsageTech companies, research labs, AI firmsGaming, entertainment, tech, research

While Cuda Programming focuses specifically on writing code using NVIDIA's CUDA platform for parallel processing, GPU Developers have a broader role that includes designing, optimizing, and implementing GPU-based solutions across various platforms and technologies. Both roles require knowledge of GPU architecture and programming languages like C/C++, but GPU Developers often work on a wider range of applications beyond CUDA-specific projects.

What are popular job titles related to Cuda Programming jobs in Virginia? For Cuda Programming jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Cuda Programming jobs in Virginia look for? The top searched job categories for Cuda Programming jobs in Virginia are:
What cities in Virginia are hiring for Cuda Programming jobs? Cities in Virginia with the most Cuda Programming job openings:
Cuda Programming jobs near you
Infographic showing various Cuda Programming job openings in Virginia as of July 2026, with employment types broken down into 13% As Needed, 42% Full Time, 24% Temporary, 18% Nights, and 3% Summer. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $112,091 per year, or $53.9 per hour.

Senior Embedded Linux Engineer

Quartermaster AI Inc

Arlington, VA • On-site

$210K - $250K/yr

Full-time

Posted 12 days ago

Job description

About Us
Quartermaster is building the world's most comprehensive maritime intelligence platform. Our SmartMast™ system transforms commercial and civilian vessels into a persistent, distributed sensing network-combining HD video, AI, radar, RF sensing, and AIS to deliver real-time maritime domain awareness at global scale. With 600+ sensors deployed across 25+ countries and more than 400,000 vessels identified outside of AIS, we are setting a new standard for what ocean surveillance and safety can look like. We are a mission-driven, high-velocity team building dual-use technology for defense agencies, coast guards, and commercial maritime operators.
Job Description
The SmartMast™ is a sophisticated, vessel-mounted edge computing platform running a dense software stack on NVIDIA Jetson hardware in maritime environments: salt air, vibration, intermittent connectivity, and real operational pressure. We need a Senior Embedded Linux / Platform Software Engineer who can own the software that runs at the tip of our spear. You will build and maintain the embedded Linux platform that integrates HD cameras, radar, SDR, AIS receivers, GPS, and thermal sensors into a unified, AI-capable sensing system. You will work at the intersection of hardware bringup, ROS2-based sensor middleware, edge inference pipelines, and the cloud connectivity layer that gets data from the vessel to our analysts in near real time. This is complex, meaningful work, and it ships to sea.
Key Responsibilities
  • Develop, maintain, and improve the embedded Linux software stack running on Jetson Orin NX and AAEON edge compute hardware aboard SmartMast units.
  • Build and integrate ROS2 nodes and packages for multi-sensor orchestration, including EO cameras (Axis), radar, SDR, AIS, GPS, and thermal imagers.
  • Design and maintain OTA software and firmware update pipelines (AWS IoT Jobs or Mender-based) that reliably deploy to a globally distributed fleet.
  • Develop sensor driver integrations and calibration routines; ensure co-collection timing and synchronization across heterogeneous sensor modalities.
  • Optimize AI inference pipelines running on-device (Jetson TensorRT, CUDA), balancing detection quality against power and thermal constraints.
  • Build and maintain the secure connectivity layer between SmartMast units and the cloud: Starlink backhaul, Peplink networking, local mesh between units.
  • Design for reliability in denied, degraded, and intermittent connectivity environments-buffering, store-and-forward, graceful reconnection.
  • Instrument the edge stack with observability tooling; triage and resolve field-reported issues from units deployed at sea.
  • Collaborate with the Hardware, Cloud Platform, and AI/ML teams to define clear interfaces across the full system stack.
Qualifications
  • 5+ years of embedded Linux or platform software engineering experience on production systems, not just prototype work.
  • Strong command of C++ and Python; deep familiarity with Linux internals (kernel drivers, systemd, networking stack, file systems).
  • Hands-on experience with the Yocto Project build framework for creating custom embedded Linux distributions, layers, and recipes.
  • Hands-on experience with ROS or ROS2 for sensor integration and inter-process communication on robotic or autonomous systems.
  • Experience with NVIDIA Jetson platforms (Nano, Xavier, Orin) or comparable edge AI hardware; familiarity with TensorRT and CUDA inference optimization.
  • Solid networking fundamentals: TCP/IP, VLAN configuration, firewall policy (iptables/nftables), and experience with Peplink or similar industrial routers.
  • Experience building or operating OTA update systems for embedded fleets; understanding of rollback, A/B partitioning, and safe deployment practices.
  • Ability to read hardware schematics and collaborate closely with electrical/hardware engineers during bringup and integration.
  • Strong debugging skills across hardware-software boundaries: serial consoles, logic analyzers, packet captures, and field log analysis.
Bonus Points:
  • Experience with SDR platforms (USRP, RTL-SDR, or similar) and RF signal processing pipelines.
  • Background in maritime, defense, or other field-deployed, environmentally hardened systems.
  • Familiarity with AIS protocol decoding, radar data formats, or NMEA message handling.
  • Experience with mesh networking protocols (802.11s, BATMAN, or custom implementations) for inter-unit communication.
  • Security experience: secure boot, device attestation, certificate management for field-deployed IoT devices.
Work Environment
  • Distributed team environment working asynchronously.
  • Start-up atmosphere with autonomy given to engineers
  • In office and flexible hours
  • Open to remote

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