Computer Vision & Edge AI Research Engineer
Role details
Job location
Tech stack
Job description
Are you passionate about building the next generation of AI-powered vision systems? Join a collaborative research and engineering team developing cutting-edge proof-of-concept solutions that bring advanced computer vision, multimodal AI, and edge computing into real-world intelligent mobility applications.
In this role, you'll work with the latest breakthroughs in AI research-from Vision Transformers and Vision-Language Models to Foundation Models and Generative AI-and transform them into working prototypes running on embedded vehicle platforms. If you enjoy solving challenging technical problems, experimenting with emerging technologies, and seeing your work deployed in real-world systems, this is an excellent opportunity.
What You'll Do
- Design, develop, and validate proof-of-concept AI and computer vision systems for intelligent mobility and in-vehicle applications.
- Research, evaluate, and implement state-of-the-art computer vision and machine learning techniques to solve real-world engineering challenges.
- Develop perception algorithms for applications such as:
- Object detection and tracking
- Semantic and instance segmentation
- 3D scene understanding
- Visual localization and mapping
- Driver and occupant monitoring
- Human behavior recognition
- Build AI solutions using modern architectures including:
- Vision Transformers (ViT)
- Vision-Language Models (VLMs)
- Foundation Models
- Multimodal AI
- Self-Supervised Learning
- Generative AI
- Evaluate the latest research publications and open-source models, adapting them into practical proof-of-concept applications.
- Develop real-time perception software that meets embedded computing constraints including latency, memory usage, power consumption, and reliability.
- Integrate camera, vehicle, and sensor data to create innovative AI-driven solutions.
- Deploy and optimize AI models for embedded and edge computing platforms using industry-standard optimization tools.
- Prototype complete end-to-end systems on research vehicles, embedded hardware, and vehicle-grade computing platforms.
- Analyze tradeoffs between model accuracy, inference speed, computational efficiency, and system robustness.
- Collaborate closely with researchers, software engineers, and systems engineers throughout the development lifecycle.
- Stay current with emerging technologies and advancements in computer vision, multimodal AI, edge AI, and intelligent mobility.
Requirements
- Master's degree or Ph.D. in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, or a related technical field.
- Strong foundation in Computer Vision, Machine Learning, and Deep Learning.
- Professional experience developing AI or computer vision applications using Python and C++.
- Hands-on experience with deep learning frameworks such as PyTorch, TensorFlow, and OpenCV.
- Experience developing and training modern deep learning models including:
- CNNs
- Transformers
- Vision Transformers (ViT)
- Vision-Language Models (VLMs)
- Multimodal architectures
- Ability to read, reproduce, evaluate, and extend published AI and computer vision research.
- Experience working in Linux development environments.
- Strong analytical, problem-solving, and software development skills.
- Excellent written and verbal communication skills with the ability to collaborate across multidisciplinary teams., * Experience with Foundation Models, Large Vision Models, and Multimodal AI systems.
- Familiarity with leading vision models and frameworks such as:
- CLIP
- Segment Anything Model (SAM)
- DINOv2
- BEV-based perception models
- Similar state-of-the-art computer vision technologies
- Experience with Generative AI, synthetic data generation, or data augmentation techniques.
- Experience developing AI software for embedded Linux systems.
- Experience optimizing AI inference using CUDA, TensorRT, ONNX Runtime, OpenVINO, or similar technologies.
- Experience deploying machine learning models on embedded AI hardware or edge computing platforms.
- Experience with NVIDIA Jetson, NVIDIA DRIVE, Qualcomm Snapdragon Ride, or similar AI computing platforms.
- Experience with ROS/ROS2, robotics, sensor fusion, or autonomous systems.
- Background in automotive, robotics, intelligent transportation systems, or advanced mobility technologies.
- Publications or significant contributions to leading AI, robotics, or computer vision conferences or journals.
What Will Help You Succeed
- Passion for applying cutting-edge AI research to solve practical engineering challenges.
- Curiosity to evaluate emerging technologies and rapidly prototype new ideas.
- Ability to balance research innovation with real-world deployment constraints.
- Strong ownership mindset with the ability to independently drive technical solutions.
- Excellent collaboration skills and enthusiasm for working in cross-functional engineering teams., * Master's (Required), * Computer vision: 4 years (Preferred)
- Python: 4 years (Preferred)
- C++: 4 years (Preferred)
- PyTorch: 3 years (Preferred)
- TensorFlow: 3 years (Preferred)
- OpenCV: 3 years (Preferred)
- Linux development: 3 years (Preferred)
- AI models: 4 years (Preferred)
- Embedded AI: 3 years (Preferred)
- Deploying Edge AI: 3 years (Preferred)
Benefits & conditions
Pulled from the full job description
- Referral program
- Professional development assistance
- Tuition reimbursement
- Parental leave
- 401(k)
- Health insurance
- 401(k) matching, * 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Professional development assistance
- Referral program
- Tuition reimbursement
- Vision insurance
Application Question(s):
- Will you now or in the future require VISA sponsorship?
- Describe an AI or computer vision project you developed from concept to deployment. What technologies did you use, what challenges did you encounter, and what was the outcome?
- Tell us about a recent computer vision or AI research paper or technology that you implemented or adapted. What changes did you make, and how did it improve your solution?