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Distributed Computing Jobs (NOW HIRING)

Achira

SWE - Distributed

New York, NY · On-site

$164K - $259K/yr

We're seeking a Software Engineer passionate about distributed computing and its applications in machine learning. You'll have the opportunity to architect and build from the ground up the ...

TekWissen

Could Architect

Atlanta, GA · On-site

Company Description Length of contract: 4 Years Distributed Computing Grid * distributed computing concepts familiarity * elastic computing and resilience * virtualization and containers ...

InterImage

Cloud Software Engineer 3

Fort George G Meade, MD · On-site

$66.50 - $86.50/hr

Provides expertise in Cloud Computing, Hadoop Eco-System including implementing Java applications, Distributed Computing, Information Retrieval (IR), and Object Oriented Design. Works individually or ...

ARSIEM

658 Cloud Software Engineer 3

Annapolis Junction, MD · On-site

$66 - $85.75/hr

Provides expertise in Cloud Computing, Hadoop Eco-System including implementing Java applications, Distributed Computing, Information Retrieval (IR), and Object Oriented Design. * Works individually ...

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Distributed Computing information

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$35K

$138.9K

$200K

How much do distributed computing jobs pay per year?

As of Jul 3, 2026, the average yearly pay for distributed computing in the United States is $138,930.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,000.00 and $164,500.00 per year, depending on experience, location, and employer.

Is distributed systems a good career?

Distributed computing is a growing field that involves designing and managing systems across multiple machines to improve performance and scalability. It requires skills in programming, networking, and system architecture, often utilizing tools like Hadoop or Kubernetes. Careers in this area can offer high demand and competitive salaries, especially for those with expertise in cloud platforms and large-scale data processing.

What is an example of distributed computing in real life?

Distributed computing in a job context involves coordinating multiple computers or servers to perform large-scale tasks efficiently. An example is processing big data analytics across a cluster of servers, which requires knowledge of distributed systems, networking, and parallel processing tools like Hadoop or Spark.

What does distributed computing do?

Distributed computing involves dividing complex tasks across multiple computers or servers to process data more efficiently and quickly. It enables systems to handle large-scale problems, such as data analysis or scientific simulations, by coordinating resources and managing communication between nodes. Professionals in this field often work with network protocols, cloud platforms, and parallel processing tools.

What are the key skills and qualifications needed to thrive as a Distributed Computing Engineer, and why are they important?

To excel as a Distributed Computing Engineer, you need a strong background in computer science, proficiency in algorithms, and experience with networked systems, often supported by a relevant degree. Familiarity with distributed systems frameworks (like Hadoop, Spark, or Kubernetes), cloud platforms (such as AWS or Azure), and knowledge of programming languages like Java, Python, or Scala is essential. Strong problem-solving, teamwork, and communication skills are crucial for designing scalable solutions and collaborating across teams. These competencies are vital to efficiently build, maintain, and troubleshoot complex distributed systems that power modern applications.

What is the highest paying job in computing?

In computing, roles such as Chief Technology Officer (CTO), Solutions Architect, and Data Science Director tend to be among the highest paying, often earning six-figure salaries. These positions typically require extensive experience, advanced skills in areas like cloud computing, machine learning, or cybersecurity, and often involve leadership responsibilities.

What is distributed computing?

Distributed computing is a field of computer science that involves dividing complex computational tasks across multiple computers or servers, which work together to solve problems more efficiently. These systems can be located in the same physical location or spread across the globe, connected via networks. Distributed computing allows for greater scalability, fault tolerance, and resource sharing, making it essential for tasks like big data analysis, scientific simulations, and cloud computing. Professionals in this field design, implement, and maintain systems that coordinate processes, manage data consistency, and ensure reliable communication between distributed components.

What are some common challenges faced by professionals working in distributed computing roles?

Professionals in distributed computing often encounter challenges such as maintaining system reliability and consistency across multiple nodes, troubleshooting issues that arise due to network latency or partitioning, and ensuring data security in a decentralized environment. Collaboration with cross-functional teams is essential, as distributed systems typically span several departments, requiring clear communication and coordinated problem-solving efforts. Adapting to rapidly evolving technologies and staying updated on best practices is also key to succeeding in this dynamic field.

What is the difference between Distributed Computing vs Cloud Engineer?

AspectDistributed ComputingCloud Engineer
Required CredentialsBachelor's in Computer Science or related; certifications like Hadoop, SparkBachelor's in Computer Science or related; cloud certifications (AWS, Azure, GCP)
Work EnvironmentData centers, high-performance clusters, on-premises or hybrid setupsCloud platforms, virtual environments, cloud service providers
Industry UsageBig data processing, scientific computing, enterprise data managementCloud infrastructure deployment, application development, DevOps
Common Search/ComparisonDistributed ComputingCloud Engineer

Distributed Computing involves managing and processing data across multiple systems to improve performance and scalability, often in on-premises or hybrid environments. Cloud Engineers focus on designing, deploying, and maintaining cloud-based infrastructure and services. While both roles require knowledge of networking, systems, and certifications, Distributed Computing emphasizes data processing frameworks, whereas Cloud Engineers specialize in cloud platforms and services.

More about Distributed Computing jobs
What cities are hiring for Distributed Computing jobs? Cities with the most Distributed Computing job openings:
What job categories do people searching Distributed Computing jobs look for? The top searched job categories for Distributed Computing jobs are:
Distributed Computing jobs near you
Infographic showing various Distributed Computing job openings in the United States as of June 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $138,930 per year, or $66.8 per hour.

SWE - Distributed

Achira

New York, NY • On-site

$164K - $259K/yr

Full-time

Posted 15 days ago

Job description

Why Achira
  • Join a world-class team of scientists, ML researchers, and engineers working together to reshape the future of drug discovery.
  • Work on cutting edge ML infrastructure at frontier scale: massive compute, massive data, and massive ambition.
  • Own impactful work end-to-end - from ideation to architecture to deployment on large-scale infrastructure.
  • Work in an environment that rewards rigor, speed, and a builder's mindset.

About the Role
Achira is building best-in-class foundation models to solve the most challenging problems in simulation for drug discovery and beyond. Atomistic Foundation simulation models (FSMs) as world models of the physical microcosm span machine learning interaction potentials (MLIPs), neural network potentials (NNPs), and diverse classes of generative models.
We're seeking a Software Engineer passionate about distributed computing and its applications in machine learning. You'll have the opportunity to architect and build from the ground up the infrastructure for our ML data generation pipelines, model training, and fine-tuning workflows across large-scale distributed systems.
Your expertise will ensure our compute clusters are efficient, observable, cost-effective, and reliable-helping us push the boundaries of ML development. If you're passionate about distributed systems, performance optimization, and cloud cost efficiency, we'd love to hear from you.
You'll be empowered to eat, breathe, and think about the orchestration of complex workloads on multiple vendors scattered anywhere on the planet. Achira is a company which lives and breaths on computation, facile access at the lowest cost for our uniquely suited workloads is a mission critical endeavor.
What You'll Do
  • Architect & Build: Design, implement, and optimize distributed compute infrastructure for ML data processing, training, and fine-tuning.
  • Optimize & Monitor: Improve cluster observability, scheduling, and resource utilization (CPU/GPU/TPU).
  • Compute Efficiency: Research and implement cost-efficient compute solutions (spot instances, auto-scaling, multi-cloud strategies).
  • Tooling: Develop tools for monitoring, debugging, and performance tuning of large-scale ML workloads.
  • Collaboration: Collaborate with ML engineers to accelerate training pipelines and reduce bottlenecks.
  • Innovation: Stay current with emerging technologies in distributed computing (e.g., Ray, Kubernetes, Spark, Slurm) and apply them strategically.

About You
  • You are excited about and have lots of experience in building or working with distributed computing frameworks (e.g., Ray, Dask, Celery)
  • You have a good grasp of parallel computing, job scheduling, and resource management.
  • You're comfortable identifying and resolving performance issues in distributed systems (profiling, bottlenecks, network overhead)
  • You've implemented solutions using cloud compute platforms (AWS, GCP, Azure) and cluster orchestration (Kubernetes, Slurm)
  • You are familiar with popular ML frameworks (PyTorch, TensorFlow, or JAX) and MLOps best practices such as model deployment and GPU performance monitoring

Eligibility
In compliance with United States federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to provide required employment eligibility verification documentation upon hire.

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