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Intern Distributed Systems Engineer Jobs (NOW HIRING)

Figma

Software Engineer, Distributed Systems

San Francisco, CA · On-site +1

$203.80K - $241.50K/yr

We're scaling fast, and we're looking for experienced distributed systems engineers across a variety of teams. Whether you're passionate about storage, compute orchestration, developer tooling ...

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How much do intern distributed systems engineer jobs pay per hour?

As of Jun 3, 2026, the average hourly pay for intern distributed systems engineer in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Intern Distributed Systems Engineer, and why are they important?

To thrive as an Intern Distributed Systems Engineer, you typically need a strong background in computer science fundamentals, programming (often in languages like Java, C++, or Go), and an understanding of distributed computing concepts. Familiarity with technical tools such as Docker, Kubernetes, cloud platforms (AWS, GCP, Azure), and version control systems like Git is commonly expected. Strong analytical thinking, effective communication, and a willingness to learn make candidates stand out in this role. These skills and qualities are crucial for solving complex distributed problems, collaborating with teams, and adapting to rapidly evolving technology environments.

What types of projects and tasks can an Intern Distributed Systems Engineer expect to work on, and how does this support their learning and growth?

As an Intern Distributed Systems Engineer, you can expect to work on collaborative projects involving the design, implementation, and testing of scalable, fault-tolerant systems. Your tasks may include optimizing data pipelines, contributing to microservices architecture, or participating in performance benchmarking. Interns often work closely with senior engineers, gaining exposure to cutting-edge technologies such as cloud platforms, container orchestration, and distributed databases. This hands-on experience, combined with mentorship and code reviews, provides valuable insights into best practices and industry standards, supporting both your technical and professional growth.

What does an Intern Distributed Systems Engineer do?

An Intern Distributed Systems Engineer assists in designing, developing, and maintaining large-scale distributed systems that enable applications to run reliably across multiple computers. Their work often involves writing and testing code, debugging issues, and collaborating with experienced engineers to solve technical challenges related to scalability, efficiency, and data consistency. Interns may also help with system monitoring and performance analysis, learning best practices for building robust and fault-tolerant systems. This role is a valuable opportunity to gain hands-on experience with cutting-edge technologies and real-world distributed computing problems.

What is the difference between Intern Distributed Systems Engineer vs Intern Software Engineer?

AspectIntern Distributed Systems EngineerIntern Software Engineer
Required CredentialsTypically pursuing a degree in Computer Science or related field, familiarity with distributed systems conceptsSimilar educational background, focus on general software development skills
Work EnvironmentFocus on designing, implementing, and testing distributed systems and networked applicationsDeveloping various software applications, often in different domains
Employer & Industry UsageUsed in tech companies working on cloud, big data, or scalable systemsCommon across many industries including tech, finance, and startups
Search & Comparison IntentPeople comparing internship roles in distributed systems and software development

Intern Distributed Systems Engineers focus on distributed architectures and networked systems, while Intern Software Engineers work on a broader range of software projects. Both roles require similar educational backgrounds but differ in specific technical focus and project types.

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What are the most commonly searched types of Distributed Systems Engineer jobs? The most popular types of Distributed Systems Engineer jobs are:
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Intern Distributed Systems Engineer jobs near you
Infographic showing various Intern Distributed Systems Engineer job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 95% Physical, 2% Hybrid, and 3% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

Staff ML Systems Engineer, Distributed Systems

FieldAI

Seattle, WA

$170K - $200K/yr

Full-time

Posted 6 days ago

Job description

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

We are seeking a Senior / Staff ML Systems Engineer to architect and build the distributed infrastructure that powers large-scale machine learning workflows across the organization.

This role sits at the intersection of machine learning, distributed systems, and platform engineering. You will be responsible for designing scalable systems that support data processing, model training, evaluation, and post-processing pipelines while enabling ML teams to efficiently develop, operate, and scale production-grade workflows.

You will play a critical role in defining the architectural patterns, tooling, and infrastructure that underpin our machine learning platform.

What You'll Get To Do
  • Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows.
  • Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms.
  • Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development.
  • Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability.
  • Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency.
  • Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development.
  • Establish best practices and engineering standards for distributed machine learning infrastructure.
  • Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs.
  • Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
What You Have
  • 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems.
  • Strong Python programming skills, including experience with concurrency, performance optimization, and systems development.
  • Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies.
  • Experience designing and scaling data pipelines or machine learning workflows.
  • Strong system design skills with demonstrated expertise in scalability, reliability, and performance optimization.
  • Experience diagnosing and resolving bottlenecks in distributed environments.
  • Ability to work cross-functionally and drive technical decisions across multiple teams.
The Extras That Set You Apart
  • Experience building infrastructure for machine learning training and inference systems.
  • Familiarity with modern ML frameworks such as PyTorch or TensorFlow.
  • Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies.
  • Experience operating Kubernetes-based infrastructure and large-scale cloud systems.
  • Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management.
  • Experience with distributed debugging, observability, and workflow orchestration platforms.
  • Proven ability to establish technical direction and influence architecture across organizations.
$170,000 - $200,000 a year

Our salary range is highly competitive with the market, but we take into consideration an individual's background and experience in determining final salary. Base pay offered may vary depending on geographic location, job-related knowledge, skills, and experience.

In addition to competitive compensation, FieldAI offers comprehensive benefits, equity participation, and the opportunity to contribute to cutting-edge advancements in AI and robotics.

Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.

Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.

Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.

Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.

We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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