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Gpu Performance Engineer Jobs in Virginia (NOW HIRING)

Adtech

AI/ML Engineer With DevOps

Ashburn, VA · On-site

$54 - $74/hr

Adtech seeks a motivated, career and customer-oriented AI/ML Engineer . This is currently a hybrid ... Experience with GPU-based infrastructure and performance optimization Clearance Requirements:

Sunayu

Contingent AI Engineer - Top Secret

Alexandria, VA · On-site

$111K - $133K/yr

GPU inference optimization Preferred Qualifications: * Experience supporting military or ... high-performance outcomes necessary to protect our country's most vital interests. Culture Our ...

ManTech

Model Operations Engineer

Ashburn, VA · Hybrid

$107K - $179K/yr

MANTECH seeks a motivated, career and customer-oriented Senior Model Ops Engineer . This is ... Experience with GPU-based infrastructure and performance optimization. Clearance Requirements:

ManTech International

Model Operations Engineer

Ashburn, VA · On-site

$71K - $96K/yr

MANTECH seeks a motivated, career and customer-oriented Senior Model Ops Engineer . This is ... Experience with GPU-based infrastructure and performance optimization. Clearance Requirements:

Booz Allen Hamilton

Computer Vision AI Engineer

Mclean, VA · On-site

$120 - $160/hr

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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Gpu Performance Engineer information

What are some common challenges faced by a GPU performance engineer when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What is a GPU performance engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

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

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

What are popular job titles related to Gpu Performance Engineer jobs in Virginia? For Gpu Performance Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Gpu Performance Engineer jobs in Virginia look for? The top searched job categories for Gpu Performance Engineer jobs in Virginia are:
What cities in Virginia are hiring for Gpu Performance Engineer jobs? Cities in Virginia with the most Gpu Performance Engineer job openings:
Gpu Performance Engineer jobs near you
Infographic showing various Gpu Performance Engineer job openings in Virginia as of August 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 87% In-person, and 13% Hybrid job distribution.

AI/ML Engineer With DevOps

Adtech

Ashburn, VA • On-site

$54 - $74/hr

Other

Re-posted 26 days ago

Job description

Adtech seeks a motivated, career and customer-oriented AI/ML Engineer. This is currently a hybrid position with two days onsite in Ashburn, VA and three days remote.

In this role, you will collaborate within a cross-functional team to develop new Artificial Intelligence/Machine Learning (AI/ML) based solutions into operational pipelines to deliver mission impact for U.S. Customs and Border Protection (CBP). The ideal candidate will have deep expertise and experience with predictive modeling lifecycles, hands-on experience with machine learning tools and frameworks, and a pragmatic, customer-centric approach to applying ML models to solve complex problems.

Each day CBP oversees the massive flow of people, capital, and products that enter and depart the United States via air, land, sea, and cyberspace. The volume and complexity of both physical and virtual border crossings require the application of solutions to aid officers in detecting threats while promoting efficient trade and travel.

Title: AI/ML Engineer with DevOps

Location: Ashburn, VA (Hybrid 2 days a week onsite)

Duration: Full time
 

Responsibilities include but are not limited to:

  • Lead the integration and deployment of trained AI/ML models into production environments (e.g., cloud, edge devices) using MLOps best practices
  • Develop and optimize model training & inference pipelines for real-time execution, and efficiently handle large-scale data processing
  • Work with data science teams to structure automated ML model health monitoring and refresh capabilities
  • Implement continuous integration, delivery and training (CI/CD/CT) workflows with commercial and open-source modeling platforms/services
  • Coordinate with Data Science and Engineering teams to build scalable feature stores for optimal model training & execution workflows 
  • Research, evaluate and recommend new tools, applications, software packages for MLOps engineering that can be adopted and approved for use in the CBP environment
  • Collaborate with cross-functional teams (e.g., Software Engineering, Data Science) to integrate and test multiple candidate AI/ML models and applications for operational assessment

Minimum Qualifications:

  • HS Diploma/GED and 20+ years of experience, AS/AA and 18+ years, BS/BA and 12+ years, MS/MA/MBA and 9+ years, or PhD/Doctorate and 7+ years
  • Expertise with MLOps tools and frameworks such as Mlflow, Kubeflow, Airflow and implementing monitoring/drift detection capabilities (e.g. Alibi, Grafana)
  • Experience with ML platforms, such as AWS Sagemaker, DataBricks or DataRobot
  • Experience automating workflow orchestration to handle both batch and real-time streaming data processing for model inference
  • Hands-on experience productionizing models, including experience optimizing for inference speed, containerization (e.g., Docker), and with multi-cloud deployment platforms (e.g., AWS, Azure, Google Cloud Platform)
  • Proficiency in Python, Scala and Java with strong understanding of high-performance computing and GPU acceleration
  • Hands-on experience with Big Data tools (e.g. Spark, Hadoop, Kafka)

Preferred Qualifications:

  • Experience with MLOps principles and tools for automated model training, testing, deployment and monitoring
  • Strong communication skills with the ability to collaborate effectively across Data Science, Data Engineering, and DevSecOps teams
  • Experience with data engineering Extract, Transform and Load (ETL) workflows across various relational/non-relational databases (Oracle/Postgres, MongoDB) and cloud endpoint services e.g. (Lambda,  GraphQL etc.)
  • Experience in using deep learning frameworks (PyTorch, TensorFlow, Keras) and computer vision libraries (OpenCV, SimpleITK, ITKm VTK)
  • Experience with biometric or image recognition algorithms and associated predictive analytics pipelines
  • Experience with GPU-based infrastructure and performance optimization

Clearance Requirements:

Kalyan Ponnam
Technical Recruiter

 | , Ext: 102

20755 Williamsport Pl, Ashburn, VA 20147

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