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Gpu Computing Jobs in Massachusetts (NOW HIRING)

Senior HPC and Quantum Systems Engineer

Westford, MA · Hybrid

$108K - $148K/yr

Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand ...

... computing and GPU optimization techniques • Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences • Understanding of modern deep learning ...

... computing and GPU optimization techniques • Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences • Understanding of modern deep learning ...

... computing and GPU optimization techniques • Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences • Understanding of modern deep learning ...

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Gpu Computing information

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$10

$19

$27

How much do gpu computing jobs pay per hour?

As of Aug 29, 2026, the average hourly pay for gpu computing in Massachusetts is $19.96, according to ZipRecruiter salary data. Most workers in this role earn between $16.54 and $21.54 per hour, depending on experience, location, and employer.

What is GPU computing?

GPU computing refers to the use of a Graphics Processing Unit (GPU) alongside a Central Processing Unit (CPU) to accelerate computational tasks. GPUs are highly efficient at performing parallel operations, making them ideal for complex calculations in fields like machine learning, scientific simulations, and graphics rendering. Unlike traditional CPUs, GPUs can process thousands of threads simultaneously, greatly speeding up tasks that involve large-scale data processing. This makes GPU computing essential in industries requiring high-performance computing solutions.

What are some common challenges faced by GPU computing professionals when optimizing code for parallel processing?

One of the main challenges in GPU Computing is efficiently restructuring code to leverage the massive parallelism that GPUs offer. Professionals often encounter issues with memory management, synchronization between threads, and minimizing data transfer between CPU and GPU to avoid bottlenecks. Additionally, debugging parallel code can be complex, as errors may not manifest consistently across runs. Collaborating with software engineers, data scientists, and hardware specialists is typical to ensure optimal performance and scalability in real-world applications.

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

To thrive as a GPU Computing Specialist, you need expertise in parallel programming, computer architecture, and a strong foundation in mathematics and algorithms, often supported by a degree in computer science, engineering, or related fields. Familiarity with programming languages like C/C++, CUDA, OpenCL, and experience with GPU hardware and high-performance computing systems are essential. Problem-solving abilities, analytical thinking, and strong collaboration skills help you innovate and work effectively on complex computational projects. These skills ensure efficient development, optimization, and deployment of GPU-accelerated solutions crucial for scientific, engineering, and AI applications.

What is the difference between Gpu Computing vs Data Scientist?

AspectGpu ComputingData Scientist
Required CredentialsKnowledge of GPU architectures, programming skills in CUDA or OpenCLDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentHigh-performance computing environments, data centers, research labsOffice settings, research institutions, tech companies
Industry UsageMachine learning, scientific simulations, graphics renderingData analysis, predictive modeling, business insights

Gpu Computing focuses on leveraging GPU hardware for high-speed processing tasks, often requiring specialized programming skills. Data Scientists analyze data to extract insights, using various tools and statistical methods. While both roles involve data and computing, Gpu Computing is more hardware and performance-oriented, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Gpu Computing jobs in Massachusetts?

For Gpu Computing jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Gpu Computing jobs in Massachusetts look for?

The top searched job categories for Gpu Computing jobs in Massachusetts are:

Infographic showing various Gpu Computing job openings in Massachusetts as of August 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $41,518 per year, or $20 per hour.

Senior Product Manager (Compute Infrastructure)

Akamai Technologies, Inc

Cambridge, MA • On-site, Remote

$139K - $250K/yr

Full-time

Medical, Life, Retirement, PTO

Re-posted 13 days ago


Akamai Technologies rating

8.0

Company rating: 8.0 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

125th of 246 rated software companies


Job description

Do you love building the foundational capabilities that power cloud computing?

Are you looking to bridge the gap between deep technical architecture and product strategy?

Come join our Cloud Technology Group!

This team is revolutionizing Akamai's cloud into a global leader for advanced computing. It involves launching GPU instances, clusters, and AI infrastructure products. These solutions empower enterprises, developers, and researchers to train models, perform inference, and address complex computational challenges. The role requires expertise in GPU technologies, innovative product strategies, and operational precision within a dynamic, results-driven environment.

Partner with the best

As a Senior Product Manager for GPU Products, you will bring technical expertise, creativity, and focus for impactful outcomes.

As a Senior Product Manager, you will be responsible for:

  • Defining product strategy and roadmap for GPU instances, clusters, and services, aligning positioning and requirements to customer and market needs.
  • Managing the entire product lifecycle, from planning to end-of-life, while creating detailed business analyses and financial models for support.
  • Developing and executing go-to-market strategies, messaging, and launch plans in partnership with marketing, sales, and solutions engineering.
  • Collaborating with ecosystem partners, aligning their roadmap and platform integration with Akamai's product objectives and technical needs.
  • Translating AI, HPC, and graphics workload needs into specifications, performance goals, and architectures to enable design success and customer engagement.
  • Overseeing GPU infrastructure lifecycle components, utilizing data-driven decisions on investments from platform evolution to implementing product End-of-Life as needed.
  • Representing various user objectives while identifying enhancements for proactive monitoring, support processes, escalation workflows, and both planned and unplanned maintenance of GPU clusters.

Do what you love

To be successful in this role you will:

  • Have 12 years of relevant experience and a Bachelor's degree in Computer Science, Engineering, or its equivalent
  • Have maintained a customer-first focus even when the customers are internal engineers and data scientists, prioritizing automation, usability, and low-friction integration for GPU workloads.
  • Demonstrate expertise in GPU architectures, CUDA ecosystem, and accelerated computing platforms, including resource management and cluster orchestration for AI workloads.
  • Have knowledge of cloud networking, GPU interconnects, and infrastructure redundancy in the context of large-scale GPU deployments.
  • Demonstrate proficiency in developing both business and technical models for GPU cloud products, including pricing, profitability, and TCO analysis.
  • Maintain familiarity with AI workload patterns, enterprise security requirements, and hardware-level APIs for GPU instances.
  • Build relationships and secure buy-in from engineering teams regarding new GPU product goals and technical requirements.

About us

At Akamai, we make life better for billions of people, trillions of times a day.
Whether you're streaming live events, scrolling social media, watching your favorite series, or managing your savings, we're the engine behind the scenes. We provide the world's most distributed platform from Cloud to Edge to help the giants of the digital world work faster and stay more secure, making the internet a better experience for everyone.
Our focus is simple:
Cloud and Edge: Running apps closer to users for instant performance.
Security: Neutralizing threats before they ever reach your data.
Content Delivery: Scaling the world's biggest moments without a glitch.
AI: Enabling our customers to build, secure, and scale AI apps on the world's most distributed cloud platform.
At Akamai, we don't just support the internet; we power and protect it, because behind every great digital experience is a massive hidden challenge. And we're the ones who solve it. When millions of people hit play or pay, Akamai ensures it just works.

Benefits at Akamai: We support your health, well-being, finances, and life beyond work. See our benefits.

FlexBase adapts to your job's needs

Akamai's FlexBase program is yet another way we show our commitment to providing employees with an exceptional workplace experience. It's not about telling employees where to work; it's about supporting employees to do their best work.
We trust our incredible employees to work in ways that suit them best: at home, in an office, or a combination of both.

Connect with us on social and see what life at Akamai is like! 

Compensation

Akamai is committed to fair and equitable compensation practices. For US based candidates only - the base salary for this position ranges from $139,300 - $250,700/year; a candidate's salary is determined by various factors including, but not limited to, relevant work experience, skills, certifications and location. Compensation for candidates outside the US will vary. The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards and an Employee Stock Purchase Plan (ESPP). Akamai provides industry-leading benefits including healthcare, 401K savings plan, company holidays, vacation (in the form of PTO), sick time, family friendly benefits including parental leave and an employee assistance program including a focus on mental and financial wellness; Eligibility requirements apply.


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