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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and optimizing multimodal AI systems, particularly in robotic perception and action generation. Proficient in implementing Transformer architectures and fine-tuning techniques to enhance model performance and integration with computer vision capabilities.
Highest-signal resume keywords
Master's Degree Or Ph.D. In Computer ScienceProgramming Skills In PythonExperience With PyTorchDeveloping Transformer-Based ArchitecturesFamiliarity With NVIDIA TensorRT
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Multimodal AIDeep LearningComputer VisionVision TransformersAction ChunkingParameter-Efficient Fine-TuningModel ProfilingPruning TechniquesVisual Feature FusionRobotic Learning
Soft Skills
Analytical SkillsProblem-Solving Skills
Tools & Technologies
NVIDIA DeepStreamTensorRTGPU-Accelerated Inference
Industry Keywords
Robotic PerceptionAction GenerationMultimodal Representation LearningVision-Language ModelsReinforcement Learning
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Develop, fine-tune, and adapt Vision Transformers and multimodal foundation models for robotic perception and action generation
- Implement action-chunking architectures, including Action Chunking with Transformers and Diffusion Policy, for long-horizon robotic task planning
- Build multi-camera fusion pipelines for VLA perception and decision-making
- Develop and optimize multimodal feature extraction pipelines integrating visual perception with robotic planning and action-generation systems
- Integrate computer vision capabilities, including face recognition and perimeter threat assessment, into multimodal AI workflows
- Apply parameter-efficient fine-tuning techniques, including LoRA, to adapt foundation models for robotic applications
- Work with Transformer architectures, visual embeddings, and vector search to improve multimodal representation and information retrieval
- Profile and optimize multimodal models through pruning and other model compression techniques
- Prepare and optimize model weights for downstream deployment using NVIDIA TensorRT and related inference optimization tools
- Evaluate model performance through experiments, benchmarking, and iterative improvements
- Collaborate with researchers and engineering teams across VLA, robotics controls, and edge AI on end-to-end system development
Requirements
What you’ll need- Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Robotics, Machine Learning, Computer Vision, or a related field
- Approximately 3–5 years of relevant experience in multimodal AI, deep learning, computer vision, foundation models, or robotic learning
- Strong programming skills in Python and hands-on experience with PyTorch
- Experience developing, training, or fine-tuning Transformer-based architectures, Vision Transformers, or multimodal deep learning models
- Understanding of vision-language models, multimodal representation learning, and integrating visual information into AI decision-making systems
- Practical experience with parameter-efficient fine-tuning methods, such as LoRA or other PEFT techniques
- Familiarity with robotic learning and action-generation approaches, including ACT, Diffusion Policy, imitation learning, or reinforcement learning
- Experience with multi-camera perception, visual feature fusion, or related computer vision pipelines
- Understanding of model profiling, pruning, quantization, or other model optimization techniques
- Familiarity with NVIDIA DeepStream, TensorRT, or GPU-accelerated inference is an advantage
- Knowledge of embeddings, vector search, and multimodal data processing is a plus
- Strong analytical and problem-solving skills, with the ability to conduct experiments and translate research findings into practical engineering solutions
- Willingness to work in a hybrid arrangement in the United States Research Cell and be available for on-site work at a designated US research location, if required for future projects
- The specific location and work arrangements will be communicated once confirmed
Benefits
Comp & perks- Work on emerging technologies at the intersection of multimodal AI, foundation models, and robotics
- Develop AI systems connecting visual perception, intelligent planning, and physical execution
- Explore advanced VLA model adaptation, action generation, and multimodal learning
- Contribute to real-world AI applications involving autonomous systems and intelligent perception
- Collaborate with researchers and engineers on challenging AI research and development projects