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Profluent

Research Intern, Computational

Emeryville, CA · On-site

$1.6K/wk

We are seeking a passionate Computational Research Intern to join moonshot projects at the intersection of bioinformatics and machine learning. This is a rare opportunity to tackle open-ended ...

Samaya AI

ML Research Intern

Mountain View, CA

Role We are seeking a highly motivated Machine Learning Research Intern to work on cutting-edge research in the fields of Natural Language Processing (NLP) and Machine Learning (ML). At Samaya, our ...

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What are Researcher Interns?

Researcher Interns are students or recent graduates who work temporarily within research teams, often in academic, corporate, or government settings. Their primary role is to assist with ongoing research projects, which may involve data collection, conducting experiments, literature reviews, or statistical analysis. The internship provides valuable hands-on experience and exposure to real-world research practices. It also helps interns develop professional skills and build networks within their chosen field.

What are the key skills and qualifications needed to thrive as a Researcher Intern, and why are they important?

To thrive as a Researcher Intern, strong analytical abilities, attention to detail, and foundational knowledge in the relevant academic field are essential, often supported by progress toward a bachelor's or master's degree. Familiarity with data analysis tools (such as Excel, SPSS, or Python), research databases, and citation management systems is typically expected. Effective communication, curiosity, and time management are standout soft skills for this role. These skills and qualities are crucial for producing accurate research, collaborating effectively, and contributing meaningful insights to the team.

What kinds of projects and daily tasks can a Researcher Intern expect to work on?

As a Researcher Intern, you can expect to be involved in a mix of literature reviews, data collection, analysis, and assisting with ongoing research projects under the guidance of senior researchers. Your daily tasks may include synthesizing academic articles, preparing presentations, managing datasets, and contributing to team meetings. Interns often have opportunities to collaborate across departments, gaining exposure to different methodologies and research tools. This hands-on experience not only builds your technical skills but also helps you understand the workflow and expectations within a research-focused environment.
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Infographic showing various Researcher Intern job openings in the United States as of June 2026, with employment types broken down into 13% Internship, 52% Full Time, 34% Part Time, and 1% Contract. Highlights an 95% Physical, 2% Hybrid, and 3% Remote job distribution.

AI Robotics Researcher Intern (Dexterous Manipulation)

NIO

San Jose, CA • On-site

Full-time

Posted 17 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.
  • GenerativeEmbodied 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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