1

Gpu Performance Engineer Jobs in Connecticut (NOW HIRING)

Staff Engineer, Sr. Manager

Groton, CT · Hybrid

$124K - $207K/yr

Translate stakeholder input into robust, high-performance, scalable, cost effective computing ... NVIDIA GPU computing. Candidate demonstrates a breadth of diverse leadership experiences and ...

Senior Cloud Engineer

Wilton, CT · On-site

$57.75 - $77.25/hr

Propose and implement improvements to system performance, reliability, cloud cost efficiency, and ... Experience supporting AI/ML workflows a plus (e.g., model deployment, GPU workloads, data ...

Cloud Engineer

Wilton, CT · On-site

$57.75 - $77.25/hr

Propose and implement improvements to system performance, reliability, cloud cost efficiency, and ... Experience supporting AI/ML workflows a plus (e.g., model deployment, GPU workloads, data ...

... performance gaming laptops, AI Laptops (Copilot+ PCs), and enterprise AI Servers & GPU ... Collaborate with internal operations and pre-sales engineers to resolve fulfillment, warranty, and ...

Posted today

Gpu Performance Engineer information

What are some common challenges faced by GPU Performance Engineers 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 Connecticut? For Gpu Performance Engineer jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Gpu Performance Engineer jobs in Connecticut look for? The top searched job categories for Gpu Performance Engineer jobs in Connecticut are:
What cities in Connecticut are hiring for Gpu Performance Engineer jobs? Cities in Connecticut with the most Gpu Performance Engineer job openings:
Infographic showing various Gpu Performance Engineer job openings in Connecticut as of July 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.

Full-time

Posted 7 days ago


General Dynamics Electric Boat rating

8.3

Company rating: 8.3 out of 10

Based on 164 frontline employees who took The Breakroom Quiz

67th of 536 rated manufacturers


Job description

The IT Advanced Technology Group at Electric Boat is seeking a highly skilled and innovative AI Engineer to join our team and support the design, development, and deployment of advanced machine learning (ML) and artificial intelligence (AI) solutions. This role involves hands-on work with data wrangling, model development, automated agents, and exploratory research into emerging AI technologies and methodologies. The ideal candidate is both technically strong and forwardthinking, with a passion for applying AI to solve complex business problems.

Responsibilities Include:

Machine Learning & Model Development

  • Design, train, validate, and deploy machine learning models using modern frameworks and best practices.
  • Build endtoend ML pipelines that include data ingestion, preprocessing, feature engineering, training, evaluation, and monitoring.
  • Optimize models for performance, scalability, and efficiency.

Data Wrangling & Analysis

  • Collect, clean, transform, and structure complex datasets from diverse sources.
  • Perform exploratory data analysis to identify trends, anomalies, and opportunities.
  • Develop automation for data processing workflows and ensure high data quality.

AI Agents & Automation

  • Create intelligent agents capable of autonomous decisionmaking, workflow automation, and contextual reasoning.
  • Integrate agents with internal systems, APIs, and knowledge bases.
  • Evaluate agent performance and iterate based on measurable outcomes.

Research & Emerging Technology Exploration

  • Stay current with the rapidly evolving AI/ML landscape, including new algorithms, architectures, tools, and best practices.
  • Prototype innovative AI solutions using cuttingedge techniques such as LLMs, RAG pipelines, multi-agent systems, and generative models.
  • Develop technical briefs, proofs of concept, and recommendations for adopting new technologies.

Collaboration & Communication

  • Work closely with software engineers, data scientists, product teams, and stakeholders to translate business needs into AI solutions.
  • Document technical designs, research findings, and model performance.
  • Provide guidance and mentorship on AI/ML concepts and toolsets.

Required:

  • Bachelor's of Science degree or Master’s degree in Computer Science, Data Science, or AI Engineering
  • 5+ years of  post-graduate related experience in developing software applications

Preferred:

  • Strong proficiency in Python and familiarity with ML libraries such as TensorFlow, PyTorch, scikit-learn, or similar.
  • Strong proficiency with application APIs, web services, and data management approaches in applications
  • Experience with data wrangling tools and technologies (Pandas, SQL, ETL systems).
  • Solid understanding of machine learning algorithms, statistical modeling, and model evaluation.
  • Experience working with LLMs or generative AI models.
  • Knowledge of cloud platforms (Azure, AWS, GCP) and containerization technologies.
  • Experience building AI agents or autonomous systems.
  • Familiarity with vector databases, RAG architectures, or multimodal models.
  • Exposure to MLOps practices (CI/CD, model monitoring, feature stores).
  • Contributions to AI research, open-source tools, or AIrelated publications.
  • Knowledge of distributed computing or GPU optimization.

  • Excellent verbal and written communication skills
  • Strong organizational and interpersonal skills
  • Ability to multi-task in a fast-paced environment
  • Ability to work on a cross-functional team as well as independently
  • Curiosity, adaptability, and enthusiasm for continuous learning.

Inside

What General Dynamics Electric Boat employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom