Research Infrastructure Engineer Jobs USA 2026

The breakthrough development in the field of Artificial Intelligence (AI) has made significant impacts across industries globally, making software engineers, machine learning experts and infrastructure professionals more sought-after than ever before. In the ever-changing landscape of AI research, the demand is growing for engineers with the ability to create the research systems, infrastructure, and developer tools that drive innovation quickly and reliably. You might be looking for Research Infrastructure Engineer Jobs USA 2026, and Thinking Machines has an exciting position available for some of the most experienced engineers out there: Research Infrastructure Engineer, Research Acceleration.

This full-time role is based in San Francisco, CA or New York, NY and will involve some facets of cutting-edge AI research as well as large-scale software engineering. The salary of this role is highly competitive between $350,000 – $475,000 per year, employee benefits are excellent, and it is possible to sponsor this position for qualified international applicants. Engineers signing-up to Thinking Machines will work closely with world-class AI researchers to improve the productivity of research and help develop next-generation AI systems by building the infrastructure.

It is open to experienced backend engineers, ML infrastructure experts or software developers interested in cutting-edge AI research, who want to become a contributor to technologies that could catalyze the future of collaborative general intelligence.

This guide will detail Research Infrastructure Engineer Jobs USA 2026, the company, role, responsibilities, educational requirements, and skills necessary for success.

About Thinking Machines

Thinking Machines is an artificial intelligence research firm specializing in improving “collaborative” general intelligence. The goal is to make smart systems that assist people in problem-solving, knowledge creation, and making AI more relevant to their specific objectives and missions.

It’s an organization that gathers together scientists, engineers, researchers and product builders that have helped develop some of the world’s most impactful AI technologies and open-source machine learning projects.

Team members have participated in the development of and/or contributions to the following technologies:

ChatGPT
Character.ai
Mistral
PyTorch
OpenAI Gym
Fairseq
Segment Anything

This background demonstrates the company’s dedication to pushing the boundaries of AI through R&D, partnerships, and engineering prowess.

Thinking Machines doesn’t just focus on developing products; its research program is also centred around building internal tools and infrastructure for researchers to be more productive, to experiment ahead of time quicker, and to build higher quality AI work.

Job Overview

The Research Acceleration, Research Infrastructure Engineer is dedicated to designing, developing and sustaining the software infrastructure needed for machine learning research.

Unlike the development of consumer-facing applications, engineers in this role develop the internal libraries, frameworks, evaluation systems and experiment management products that researchers rely on on a daily basis.

It requires strong collaboration with research groups to understand technical issues and eliminate bottlenecks while continually optimizing systems that support the development of AI.

Position Highlights

Full-time employment
Job Area: San Francisco, CA or New York, NY
Annual salary of $350,000–$475,000
Eligible, visa sponsorship available.
Cooperation with cutting-edge AI experts
To participate in developments for the next generation of machine learning infrastructure to experience contributing to the next-generation machine learning infrastructure
From concept to completion in the design, development and delivery of engineering projects.

Ideal for engineers who love to write out the design and solve complex technical issues and want to empower research teams to work with greater innovation.

What is a Research Infrastructure Engineer?

Research Infrastructure Engineers develop the underpinning systems which enable faster, more reliable, and easier machine learning research.

Rather than user software, they build the engineering software that enables researchers to work along the entire AI development life cycle.

These systems render assistance to the researchers:

Study and train Machine Learning models.Study and train ML models.
Evaluate model performance
Track experiments
Improve reproducibility
Monitor training processes
Scale distributed workloads

The success of this role rests on the ability for the infrastructure to support researchers to conduct high standards of research in a manner that is free from unnecessary technical barriers.

Key Responsibilities

Thinking Machines is looking for Research Infrastructure Engineers to be instrumental in developing robust systems to enhance research productivity that meet the highest standards for usability, performance and scalability.

Design Research Infrastructure

The key task is the design and maintenance of the software infrastructure that is utilized across the scope of the research organisation.

This includes developing:

Evaluation frameworks
Reinforcement learning (RL) training systems
Experiment tracking platforms
Visualization tools
Shared engineering utilities

Scientists would be able to spend more time advancing the research of machine learning and less on managing the tools that support it.

Construct scalable machine learning pipelines

Create scalable machine learning pipelines. In many cases, AI studies today will include working with large amounts of data and computationally heavy tests.

The Engineers in this role are responsible for creating high throughput pipelines for:

Distributed evaluation
Reward modeling
Multimodal assessment
Large scale machine learning pipeliness

Scalable engineering solutions help keep research systems constantly efficient, allowing them to scale to meet increased demand.

Improve Research Reproducibility

Reproducibility is a key component of scientific research.

The job of the Engineer is to create systems that improve:

Experiment traceability
Quality control
Version management
Monitoring
Observability

The features enable researchers to accurately duplicate previous experiments and to have confidence in the output model evaluation and model training results.

Work in partnerships with researchers

This is unlike the conventional software engineering job as it is a collaborative process with the researchers of AI.

Engineers work directly with research teams to:

Identify workflow bottlenecks
Understand technical challenges
Gather user feedback
Improve research tooling
Prioritize infrastructure improvements

Engineers continuously optimize systems, as they view internal engineering tools as products and they increasingly are making them accessible to researchers and their needs.

Collaborate with Cross-functional Teams.

Research infrastructure brings together a number of technical fields within the organization.

To what extent do engineers work with:

Infrastructure teams
Data engineers
Product teams
Machine learning researchers

There is excellent communication and teamwork to ensure that research tools work well across the company’s technical ecosystem.

Essential Skills

A Research Infrastructure Engineer must possess both solid software engineering skills and knowledge about the machine learning research process.

Software Engineering Expertise

Experience should include constructing large, robust and sustainable software systems to serve large engineering organizations.

Building blocks of good engineering practice:

Clean code
Testing
System reliability
Scalability
Maintainability

Such principles guarantee the dependability of infrastructure as research grows.

Backend Programming

Thinking Machines mainly is used as:

Python
Rust

Candidates need to have good knowledge of one of these backend programming languages.

Designing APIs and automation tools, and designing distributed systems can further reinforce an application.

End-to-End Project Ownership

RI Engineers are expected to hold projects from inception to deployment.

Duties will include:

Designing solutions
Implementing features
Testing systems
Deploying infrastructure
Maintaining production reliability
Continuously improving performance

Ownership calls for technical expertise, as well as excellent organizational skills.

Collaboration Skills

Interpersonal skills with researchers, engineers and cross functional team are essential in this position.

Successful candidates should NOT be uncomfortable:

Gathering requirements
Discussing technical ideas
Explaining engineering decisions
Operating on a multi-Organization basis

The higher the level of collaboration, the more tools from research will be adopted.

Problem-Solving

Technical difficulties in AI research constantly keep changing.

Engineers should enjoy:

Engaging in complex engineering problem-solving.Solving complex engineering problems.
Optimizing workflows
Removing technical bottlenecks
Improving developer productivity

Now as much as always, creative thinking and analytical problem solving skills are vital.

Minimum Qualifications

Thinking Machines is looking for candidates with solid engineering principles and engineering experience.

Applicants should possess:

Bachelor degrees in Computer Science, Engineering, Machine Learning, or related areas is a plus (or equivalent experience), but not required
Proficient programming skills.
Knowledge of Python or Rust programming language.
Project management skills, from initiation through implementation:
Gain experience in cross-functional teams with multiple technical teams working together
Experience working in collaborative technical environments involving multiple cross-functional teams

These certifications will complement the advanced research infrastructure built on AI to serve advanced AI research.

Preferred Qualifications

The following experience, by no means essential, will support an application:

Engineer tools used by machine learning researchers.
Creating sets for training ML or an evaluation library.
Experience in using tracking systems for experiments.
Independent donations to open source machine learning projects.
Technical publications focusing on machine learning systems/ infrastructure matter
Coercing up close and personal with Machine Learning researchers.
Familiar with distributed systems
Experience with cutting-edge ML toolkits such as PyTorch or Jax
Knowledge about distributed computing technologies, for example Ray or Spark
Test out extensive evaluation pipelines.Test large scale evaluation pipelines.

Eligibility for these extra qualifications will be based on experience in research-oriented engineering environments, and some candidates may benefit in the process of job placement.

Salary and Compensation

The Research Acceleration position with Thinking Machines is one of the most alluring features it can offer a candidate; the salary! Based on background, technical skill and experience, the company provides an annual expectation between $350,000-$475,000 USD.

This compensation aligns with the high level of specialized skills needed for the position and the important contributions of Research Infrastructure Engineers in the quest to advance AI research. Engineers can help to enhance research productivity throughout the organization by developing scalable infrastructure, experiment tracking systems, and research tools.

Staff are provided with all the benefits they would expect to receive in a successful company, as well as a competitive salary, with their careers encouraged to have long-term prospects through the benefits covers.

Employee Benefits

Thinking Machines provides a complete employee benefits program to foster health and wellness, work-life balance, and career development.

Complete Health Insurance Program

Qualifying workers are entitled to these health care benefits:

Health insurance
Dental insurance
Vision insurance

Such benefits boost the access to important healthcare services for employees’ families so as to ensure financial protection.

Unlimited Paid Time Off

Work/life is important for sustaining productivity and creativity over time.

At Thinking Machines, we provide Unlimited paid time off to support our employees in maintaining work/life balance, being able to take leave when they need it and keeping up with the team and project

Relocation Support

Thinking Machines provides relocation benefits (where applicable) as part of their services to candidates moving to San Francisco, California or New York, New York.

Visa Sponsorship

International candidates can avail this opportunity as a side benefit of the visa sponsorship..

According to Thinking Machines, they issue visas to qualified candidates. The company will make a best effort for all eligible applicants and positions to obtain a visa – however this process can not be guaranteed on any individual basis.

It’s a good time for professionals with a background in software engineering or machine learning infrastructure who are looking to pursue a career in the U.S.

Why would anybody want to work for Thinking Machines?

Thinking Machines is a faculty of established scientists, engineers, and researchers with a background in some of the most impactful AI technologies and open source machine learning projects.

In the context of this environment engineers have the opportunity to:

Work at the interface of AI practitioners and researchers up close
Use understanding and independent study to tackle complex engineering tasks
Development tools that are used throughout research groups
Serve the future of AI infrastructure
Create projects that have a substance in their long-term impact

Along with developing features, engineers are involved in enhancing the systems that support research teams in their innovation.

Career Growth Opportunities

The trade and visible growth of the AI organization are excellent chances for ongoing learning and professional development.

As a Research Infrastructure Engineer, you may gain experience in areas such as:

Distributed systems engineering
Machine learning infrastructure
AI research tooling
Experiment management platforms
Research reproducibility
Performance optimization
Backend software engineering
Cross-functional technical leadership

Therefore, the skills and expertise acquired in this position could enhance advancement in AI, machine learning engineering, infrastructure engineering, and enterprise software development.

Tips for Applying

Application preparation is an essential aspect of applying for Research Infrastructure Engineer Jobs USA 2026 and can greatly enhance your prospects of securing a positio

Tailor Your Resume

To draw attention to the following on your resume:

Software engineering experience
Backend development
Infrastructure projects
Machine learning systems
Distributed computing
Open-source contributions

Use measurable outcomes and technical achievements in the project, if possible.

Show/reiterate Relevant Programming Skills

Bloom through back-end coding skills in:

Python
Rust

Also include if relevant, experiences with distributed systems, scalable architectures, or cloud infrastructure.

Showcase Collaboration Experience

Thinking Machines is a firm believer in the skills of multi-team players.

Include a few examples of working collaboratively with:

Researchers
Product teams
Infrastructure engineers
Data engineers
Cross-functional technical organizations

Duties include strong communication skills and teamwork.

Display Research Infrastructure Practice

Preferably candidates who have experience building:

Experiment tracking systems
ML training frameworks
Evaluation platforms
Research tooling
Monitoring systems

must surely add their achievements to the CV or portfolio.

Discuss Contributions To Open Source Projects

Do not forget to add any contributions made to open source projects, technical documents or machine learning. For applicants who have contributed in some way to open source projects, technical documents or a machine learning project, be sure to add it in your application.

Who Should Apply?

ESSENTIAL OCCUPATIONAL SKilletS: This position is well suited for individuals who:

Latched in to the challenge of solving engineering problems that are more complex
Have strong backend software engineering skills
Avid enthusiasts of Artificial Intelligence
Like building infrastructure rather than end-user applications
Be involved in teamwork with researchers.
Desire to engage in meaningful work with AI functions that impact the world.
Enjoy design – through – deployment of projects
Look for opportunities in a research-based engineering setting

It could be interesting for the candidate with such a background to apply for roles related to machine Jobs in machine learning frameworks, infrastructure engineering, distributed computing, or platform building may be of interest to candidates with this kind of background.

FAQ

Where is this position located?

San Francisco, CA or New York, NY campus based position.

What is the salary range?

For this position, the annual salary is between 350000 and 475000 USD.

Can the immigrants be sponsored?

Yes, Thinking Machines states that the role comes with visas to qualified applicants and visa process support where appropriate.

Which programming languages are preferred?

The enterprise generally uses:

Python
Rust

you must have command in above two programming languages.

What are the education requirements?

You require a Bachelor’s degree in Computer Science, Engineering, Machine Learning or related field.
What are the desired experiences?

Preferred experience includes:

ML research infrastructure
Experiment tracking systems
Distributed systems
PyTorch or JAX
Ray or Spark
Open-source ML projects
Research tooling

What is the advantage of offering them?

Eligible workers can get:

Health insurance
Dental insurance
Vision insurance
Unlimited Paid Time Off
Paid parental leave
Relocation support
Visa sponsorship

Conclusion

Thinking Machines’ Research Infrastructure Engineer 2026 role is truly a unique professional opportunity for any software engineer interested in AI, scalable systems, and research in any field. Through the collaborative effort of working on a team to improve collaborative general intelligence, engineers can help develop the systems that will enable researchers to create new potential technologies for creating AI more effectively.

Competitive salary from $350,000 to $475,000 with comprehensive employee benefits, unlimited PTO, relocation and visa sponsorship for qualified applicants, this job opportunity offers a good combination of powerful technical challenges and stellar compensation to experienced professionals.

This position provides not only compensation, but also allows you to work alongside researchers, design and build scalable machine learning infrastructure, and participate in tool building efforts that aim to aid cutting-edge AI research. This is a great position for an engineer who is strong in software development, with experience in backend programming (Python or Rust), and has an interest in research infrastructure.