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How much do internship fieldwork research jobs pay per month?

As of Aug 1, 2026, the average monthly pay for internship fieldwork research in the United States is $6,439.50, according to ZipRecruiter salary data. Most workers in this role earn between $4,416.67 and $7,666.67 per month, depending on experience, location, and employer.

What is the difference between Internship Fieldwork Research vs Field Research Assistant?

AspectInternship Fieldwork ResearchField Research Assistant
Required CredentialsTypically students or entry-level with relevant courseworkOften requires some college coursework or related experience
Work EnvironmentSupervised, educational setting often with academic institutionsField sites, research labs, or outdoor environments
Employer & Industry UsageUniversities, research institutes, NGOsResearch organizations, environmental agencies, universities
Common Search & Comparison IntentUnderstanding internship roles in researchClarifying responsibilities of research assistants

Internship Fieldwork Research typically involves students gaining hands-on experience under supervision, focusing on learning and supporting ongoing projects. Field Research Assistants are more experienced, assisting with data collection and analysis in field settings. Both roles are vital in research projects but differ mainly in experience level and scope of responsibilities.

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What are the most commonly searched types of Fieldwork Research jobs? The most popular types of Fieldwork Research jobs are:
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Internship Fieldwork Research jobs near you

Robotics Research Internship-Locomotion & Planning (Fall 2026)

FieldAI

Irvine, CA

$45 - $60/hr

Other

Re-posted 16 days ago

Job description

About the Internship
 
Field AI is building the future of autonomy-from rugged terrain to real-world deployment. We're on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments. 
 
We are offering a Summer 2026 internship focused on learning-based locomotion and planning for PhD students interested in advancing autonomous legged robot capabilities. As a research intern, you will work at the intersection of reinforcement learning, locomotion control, and learned planning, developing integrated systems that enable robots to move and navigate intelligently through complex, unstructured environments.
 
You will collaborate closely with Field AI research scientists and engineers to design experiments, develop locomotion and planning systems, and validate ideas in simulation and on real hardware. This internship emphasizes building tightly integrated learning-based systems that connect low-level locomotion with high-level planning, translating research into practical, deployable capabilities for real-world robotics.
What You'll Get To Do
  •  
Advance RL-Based Locomotion and Learned Planning Research
 
  • Design, implement, and evaluate reinforcement learning pipelines that tightly integrate locomotion control with learning-based planning.
  • Explore how learned planners can inform and adapt locomotion behaviors across varied terrain and dynamic conditions.
  • Contribute to research projects from early-stage ideas through simulation experiments and on-robot validation.
 
Bridge Locomotion and Planning Across the Sim-to-Real Gap
 
  • Develop and refine sim-to-real transfer strategies, including domain randomization, system identification, and adaptive methods, for integrated locomotion-planning systems.
  • Build and leverage GPU-accelerated simulation environments (Isaac Gym, Isaac Lab, MuJoCo) for scalable training and evaluation.
  • Test and iterate on policies using real legged robot platforms in unstructured environments.
 
Build Systems That Connect Research to Deployment
 
  • Translate research concepts into working robotic systems tested on real hardware.
  • Develop experimental setups and tooling to support data collection, evaluation, and reproducibility.
  • Help ensure locomotion and planning systems are robust, field-relevant, and ready for iterative improvement.
 
Collaborate Across the Full Robotics Stack
 
  • Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.
  • Incorporate real-world telemetry and field data to refine models and improve generalization.
  • Engage with researchers and engineers across the team to align experiments with broader autonomy goals.
 
Rapidly Iterate and Learn
 
  • Prototype quickly, run experiments in simulation and on hardware, and analyze results rigorously.
  • Balance exploratory research with concrete deliverables over the course of the internship.
  • Debug system-level issues spanning simulation, software, hardware, and learning.
What You Have
  • Current PhD student in Robotics, Computer Science, Mechanical Engineering, AI/ML, or a closely related field.
  • Research experience in reinforcement learning for continuous control, locomotion, or learning-based planning.
  • Strong foundation in contact dynamics, control theory, and kinematics.
  • Proficiency in Python and/or C++, with experience using robotics or ML tooling.
  • Familiarity with physics-based simulators such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet.
  • Experience designing experiments and evaluating results on robotic systems (simulation or hardware).
  • Curiosity, initiative, and a strong interest in building autonomous systems that operate in the real world.
The Extras That Set You Apart
  • Hands-on experience with legged robot platforms (quadrupeds, wheeled-quadrupeds, bipedal systems, or exoskeletons).
  • Experience with sim-to-real transfer for locomotion or planning policies.
  • Background in learning-based planning, motion planning, or terrain-adaptive control.
  • Familiarity with ROS or ROS2.
  • Publications, preprints, or open-source contributions in locomotion, RL, planning, or control.
  • Experience deploying neural network controllers on resource-constrained or real-time robotic platforms.
  • Interest in bridging cutting-edge research with practical, field-ready robotic systems.
$45 - $60 an hour
Our salary range is generous and we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. 
 
Field AI Onsite Work Philosophy
 
At Field AI, we believe the most effective way to collaborate and solve complex challenges is by working together in person. This is a fully onsite role, and candidates will be expected to work from our Irvine, CA office. In-person engagement is essential to our success, and we offer flexible working hours to support focus and work-life balance.
 
Why Join Field AI?
We are solving one of the world's most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.
 
You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.
 
Be Part of the Next Robotics Revolution
To tackle such ambitious challenges, we need a team as unique as our vision - innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We're seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.
 
Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!
 
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. 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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