th an educational background as well as work experience relevant to Advanced Computer Vision and Machine Perception. Minimum bachelors/masters degrees in Computer Science/Engineering or relevant fields (or PhD/PostDoc level for the Senior positions), as well as relevant experience. The candidates should have strong Mathematical, Statistical and Programming as well as Computer-Vision and Machine-Perception-specific skills, including AI/ML/Deep Learning, and hands-on experience in relevant packages such as OpenCV, SciKit-Image, Tensorflow, and PyTorch. An existing project portfolio and codebase in github as well as specialization in certain application areas would be highly desirable. The candidate should have the ability to communicate with other AI/ML technical team members as well as business stakeholders to understand and interactively negotiate system requirements and objectives, translating them to scope agreement documents, including not only system and software archit!
ectural choices, but also expected timelines, dataset specific!
ations, and evaluation methods and potential achievable KPI’s.
*Key Responsibilities:*
• Proficient utilization of computer vision, machine perception, and other relevant tools, such as OpenCV, SciKit-Image, Tensorflow, and PyTorch, towards creating state-of-the-art computer vision and machine perception systems
• Choice and utilization not only of on-prem but also of cloud-based computer vision and machine perception services, as offered by Google, Azure, and other such providers
• Training and testing dataset discovery, creation, choice, requirements analysis, as well as model creation or transfer-learning
• Creation of systems that have strict response-time, robustness, and/or computational resource constraints
• Knowledge of, choice and utilization of various imaging and perceptual sensors, including (but not limited to) visible, infrared, SWIR, multispectral, as well as laser-ranging sensors and medical-imaging imaging.
• Desirable (not required for all candidates): Design, implementation, and evaluation of non-imaging perceptual systems which might include auditory, tactile, and olfactory systems, among others.
• Desirable (not required for all candidates): Designing, implementing, and evaluating real-time closed-loop perception-action systems, as well as multi-sensor-fusion systems
• Desirable (not required for all candidates): Demonstrating excellence in specific application areas of machine vision and perception, such as aerospace, energy, defense/security, biomedical, and maintaining our use-cases, reusable components (models/datasets/pipelines etc)
• Desirable (not required for all candidates): Application of deep knowledge and extensive experience towards Affective and Human-Centered Perception Systems, including Whole-Body-Interfaces, Brain-Machine Interfaces, Eye Tracking devices.
• Team-working, team-creation and -leadership (for higher seniority positions), as well as stakeholder communication, including understand and interactively negotiating system requirements and objectives, translating them to scope agreement documents, including not only system and software architectural choices, but also expected timelines, dataset specifications, and evaluation methods and potential achievable KPI’s.
*Invitation to Join Aramco Digital:*
The role of Computer Vision and Machine Perception Specialists for the Aramco Intelligence Business Unit of Aramco Digital represents a unique opportunity to participate in the newly-founded digital entity of the Aramco, one of the world's largest integrated energy and chemicals companies, delivering societal and economic benefits to people and communities around the globe who rely on the vital energy it supplies, which is furthermore committed to playing a leading role in the energy transition. The chosen candidate will play a pivotal role in driving innovation, working alongside a team of experts dedicated to developing solutions that enable businesses to reach unprecedented levels of efficiency, decision-making accuracy, and impact towards a better world.
Kindly send your CV to nmav@alum.mit.edu for more information!