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Robotics Research Internship Jobs (NOW HIRING)

Applied Intution

We are looking for multiple passionate Research Interns to join the Research Group at Applied ... Passion for next-generation, scalable autonomy and robotics Don't meet every single requirement? If ...

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Fujitsu

Research Intern - FRA

Pittsburgh, PA · On-site

Interns will research and build systems that integrate LLMs, machine learning, computer vision, and robotics models, with the goal of publishing at top-tier conferences. Selected candidates will work ...

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How much do robotics research internship jobs pay per month?

As of Sep 4, 2026, the average monthly pay for robotics research internship 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 a robotics research internship?

A Robotics Research Internship is a temporary position, usually for students or recent graduates, where individuals work with a team to contribute to research projects in the field of robotics. Interns may assist with designing, building, and programming robots, conducting experiments, analyzing data, and writing reports or papers. The internship provides hands-on experience with the latest robotics technologies and research methodologies, often in academic, industrial, or laboratory settings. It is a valuable opportunity to gain practical skills, learn from professionals, and explore career paths in robotics and related fields.

What types of projects and collaboration opportunities can I expect during a robotics research internship?

As a Robotics Research Intern, you’ll typically work on cutting-edge projects such as developing algorithms, building prototypes, or testing robotic systems. Interns often collaborate closely with experienced engineers, researchers, and sometimes other interns, gaining exposure to multidisciplinary teams that may include software, hardware, and AI experts. You’ll participate in regular team meetings, report on your progress, and may have opportunities to contribute to academic papers or presentations. This collaborative environment helps interns build technical skills, learn about recent advancements in robotics, and expand their professional network.

What are the key skills and qualifications needed to thrive as a robotics research intern, and why are they important?

To thrive as a Robotics Research Intern, you typically need a solid background in robotics, computer science, or engineering, with coursework or experience in areas like control systems, AI, and mechanical design. Familiarity with programming languages (such as Python, C++, or ROS), simulation software, and robotics hardware platforms is highly valued. Strong analytical thinking, problem-solving skills, and effective communication help interns collaborate on research projects and present findings clearly. These skills and qualities are crucial for contributing to innovative robotics research and successfully supporting complex project goals.
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Robotics Research Internship jobs near you
Infographic showing various Robotics Research Internship job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $77,274 per year, or $37.2 per hour.

AI Robotics Researcher Intern (Dexterous Manipulation)

NIO

San Jose, CA • On-site

Full-time

Re-posted 21 days ago

Job description

JOB DESCRIPTION

About NIO

NIO is a pioneer and a leading company in the premium smart electric vehicle market. Founded in November 2014, NIO's mission is to shape a joyful lifestyle. NIO aims to build a community starting with smart electric vehicles to share joy and grow together with users.

NIO designs, develops, jointly manufactures and sells premium smart electric vehicles, driving innovations in next-generation technologies in autonomous driving, digital technologies, electric powertrains and batteries. NIO differentiates itself through its continuous technological breakthroughs and innovations, such as its industry-leading battery swapping technologies, Battery as a Service, or BaaS, as well as its proprietary autonomous driving technologies and Autonomous Driving as a Service, or ADaaS.

NIO's product portfolio consists of the ES8, a six-seater smart electric flagship SUV, the ES7 (or the EL7), a mid-large five-seater smart electric SUV, the ES6, a five-seater all-round smart electric SUV, the EC7, a five-seater smart electric flagship coupe SUV, the EC6, a five-seater smart electric coupe SUV, the ET7, a smart electric flagship sedan, and the ET5, a mid-size smart electric sedan.

About the Position

We are looking for an outstanding AI Robotics Research Intern to join the team at NIO. This role operates at the cutting edge of embodied AI and dexterous manipulation, with a specific focus on utilizing large-scale foundation models and human data-based learning to empower robots with physical world intelligence.
As an intern, you will tackle the fundamental challenges of dexterous manipulation by harvesting human-object interaction data from diverse sources-ranging from unstructured web videos to high-fidelity human glove-collected data. Your work will involve translating these rich human insights into executable robotic behaviors, bridging the gap between human dexterity and machine execution. You will be responsible for deploying these policies on real hardware, to perform complex, contact-rich tasks in real-world environments
Project Scope
  • Learning from Human Demonstrations: Develop and refine scalable frameworks for the transfer of human-object interaction skills to diverse robotic embodiments.
  • Large-Scale Data Synthesis: Architect autonomous pipelines to process vast amounts of visual data and human glove-collected data, extracting the spatial and contact-rich information necessary for generalist robot training.
  • Generative Embodied AI: Implement state-of-the-art generative architectures to synthesize physically grounded, high-fidelity trajectories based on human reference motions.
  • Unified Policy Training: Explore cross-embodiment representations that enable joint training on human and robot data to improve generalization in unstructured environments.
  • Sim-to-Real Deployment: Research and optimize distillation and retargeting techniques to bridge the gap between simulation-trained policies and physical robotic deployment.
  • Semantic Scene Understanding: Utilize vision-language foundation models to autonomously segment skills and extract task-relevant parameters from complex human activities.
Deliverables (End of Internship)
  • A robust pipeline for converting human multi-modal data into actionable robot motor skills.
  • A successful sim-to-real validation of a dexterous manipulation policy on a physical humanoid or multi-fingered platform.
  • A high-quality technical manuscript or demo suitable for internal review or submission to a top-tier robotics conference.
Qualifications
  • Master's or Ph.D. in Robotics, Computer Science, Artificial Intelligence, Mechanical/Electrical Engineering, or related fields.
  • Strong technical foundation in robot learning and control, including areas such as reinforcement learning, imitation learning, world modeling, or representation learning for agent-environment interactions.
  • Practical experience implementing and fine-tuning Generative Models and Transformer architectures.
  • Hands-on experience with robotic manipulation systems, particularly involving contact-rich interaction, grasping, or multi-sensor perception (e.g., tactile, force/torque, proprioception).
  • Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX, TensorFlow), with experience using robotics middleware or simulation tools (e.g., ROS/ROS2, MuJoCo, Isaac Sim, PyBullet).
  • Demonstrated ability to implement, experiment, and iterate on research ideas, including evaluating methods through empirical results on simulated or physical robotic systems.
  • Strong analytical and system-building skills, with the ability to work across simulation, learning, perception, control, and real robot deployment as part of a larger technical team.
Preferred Qualifications
  • Ph.D. (or Ph.D. candidate expecting graduation within 6-12 months).
  • Prior experience with dexterous manipulation, multi-finger robotic hands, in-hand manipulation, or grasp optimization beyond parallel-jaw grasping.
  • Experience deploying learning-based policies on real robotic hardware, including exposure to sim-to-real transfer challenges such as contact mismatch, compliance, sensing noise, or latency.
  • Familiarity with contact modeling, tactile sensing, force/torque feedback, or low-level control interfaces for manipulation.
  • Background in 3D perception, geometric representations, or learned representations relevant to physical interaction.
  • Experience with reinforcement learning in continuous control, model-based methods, or real-time policy execution.
  • A strong interest in building robust, real-world robotic systems, and motivation to see research ideas validated through physical experiments rather than simulation alone.
  • Track record of publications in top AI or robotics conferences (CoRL, ICRA, IROS, RSS, NeurIPS, CVPR, ICML).

Compensation:

The US base salary range for this full-time position is $38.00 - $46.00.

  • Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

  • Please note that the compensation details listed in US role postings reflect the base salary only. It does not include discretionary bonus, equity, or benefits.

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