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

Why RoboForce

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

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.

Requirements

• 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.

Bonus Qualifications

• 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, infe