AI Research Engineer Intern (PhD), Real-Time Inference for Embodied AI

We are seeking an **AI Research Engineer Intern (PhD)** to join us in building the next generation of **Embodied AI systems** for robotics, with a focus on **real-time model inference, systems optimization, and deployment efficiency**. In this role, you will work at the intersection of **foundation models, robotics, and high-performance ML systems**, helping make advanced robot intelligence practical for real-world deployment. You will collaborate with a world-class team of researchers and engineers to optimize model serving, reduce latency, improve throughput, and enable reliable on-robot inference for embodied decision-making. This is a highly applied research role with opportunities to contribute to impactful systems work and, where appropriate, **research publications at top-tier venues**. ### **Responsibilities** * Research and develop techniques to enable **real-time inference** for embodied AI models deployed on robotic platforms. * Optimize inference performance for models such as: + **Vision-Language-Action (VLA) models** + **World models** + **Multimodal transformer-based policies** + **Perception and state estimation models** used in robot control loops * Improve model latency, throughput, memory efficiency, and system reliability through methods such as: + model compression + quantization + distillation + batching and scheduling optimization + KV-cache / decoding optimization + graph compilation and kernel-level acceleration * Collaborate with robotics, infrastructure, and hardware teams to integrate optimized models into real robot stacks and edge/on-device systems. * Design benchmarking pipelines for evaluating end-to-end performance, including control frequency, action latency, and system robustness under real deployment constraints. * Explore tradeoffs between model quality and runtime efficiency to support practical deployment in real-world robotic tasks. * Contribute to internal technical reports, system design discussions, and **publications** where appropriate. ### **Qualifications** * Currently pursuing or recently completed a PhD in Computer Science, Electrical Engineering, Robotics, Machine Learning, Systems, or a related field. * Strong background in **machine learning systems**, **model inference optimization**, or **efficient deep learning**. * Experience optimizing modern ML models for production or low-latency deployment. * Hands-on experience with one or more of the following: + real-time inference systems + efficient transformer inference + model compression, pruning, quantization, or distillation + GPU performance optimization + deployment frameworks such as TensorRT, ONNX Runtime, XLA, TVM, Triton, or similar systems * Proficiency with deep learning frameworks such as **PyTorch**, **JAX**, or **TensorFlow**. * Strong programming and systems skills, including experience with performance profiling and debugging. * Ability to work across the stack, from model architecture to runtime systems and hardware-aware optimization. * **Requires 5 days/week in-office collaboration with the team.** ### **Preferred Skills** * Familiarity with **Embodied AI**, **robot learning**, or **robotics foundation models**. * Experience optimizing **multimodal** or **autoregressive** models for low-latency inference. * Understanding of robotics system constraints such as control-loop timing, sensor fusion latency, and edge compute limitations. * Experience with deployment on embedded or edge hardware for robotics. * Exposure to compiler-based optimization, CUDA programming, custom kernels, or distributed inference systems. * Interest in co-design across **model architecture, inference runtime, and robotic execution**. ### **Why Join Us** * Work on high-impact problems at the frontier of **AI systems and robotics** * Help turn cutting-edge embodied AI models into practical real-world robotic capabilities * Collaborate with a deeply technical team spanning research, systems, and hardware * Gain hands-on experience with challenging deployment problems in real robotic settings * Opportunity to contribute to **research publications** and advance the state of the art in efficient embodied AI