Computer Vision Engineer
Role details
Job location
Tech stack
Job description
An innovative education technology company based in Spain is seeking an experienced professional to enhance their Computer Vision algorithms and tackle existing challenges in online learning. The role involves formulating innovative methods to prevent fraud, developing machine learning models, and collaborating with a dedicated AI team. The ideal candidate will have strong skills in Computer Vision and Deep Learning, and a passion for impactful education technology., Build trust: By being honest and supportive, actively listening, being consistent, collaborating across teams and functions, and taking responsibility for your acts and words.
Dare to commit: Decide to show up fully and consistently while seeing things to their logical or necessary conclusion. Make things work.
Always go beyond your limits: Through self-awareness, curiosity and initiative. With determination, discipline and DOING.
Stay Agile: Be able to quickly adapt or evolve in response to changing circumstances in a highly responsive way so that we deliver our service to meet and exceed customer expectations and in a timely manner. Position Summary
Are you someone who likes a challenge and wants to take responsibility? You will be part of our amazing AI team with the aim of improving and enhancing our Computer Vision algorithms or creating new ones giving answer to existing and new challenges. In addition to that, you will work with other technologies such as audio analysis or NLP. Responsibilities
- Formulate innovative approaches to combat fraud using computer vision and machine learning.
- Create new features, train new models, deploy them into production environment.
- Contribute by extending and improving our ML frameworks and platform, creating next-generation capabilities.
- Build and deploy solutions to interesting computer vision or machine learning problems including document data extraction, fraud detection or biometric verification challenges. Design and implement efficient pre-processing steps around digital images or video files.
- Work alongside other machine learning and computer vision specialists in order to deliver on both short term objectives and long term goals.
- Support and guide other engineers in learning about, applying and delivering product features driven by machine learning techniques.
Requirements
- 3+ years of experience in Computer Vision and Deep Learning, ideally around 5.
- Experience working in investigation or start-up environment.
Responsabilidades
- Formulate innovative approaches to combat fraud using computer vision and machine learning.
- Create new features, train new models, deploy them into production environments.
Conocimientos
Computer Vision Deep Learning Python development Machine Learning AWS knowledge, * Specific training in Computer Vision and Deep Learning.
- 3+ years of experience in Computer Vision and Deep Learning, ideally around 5.
- Experience working in investigation or start-up environment.
- AWS knowledge (Lambda, Sagemaker, Rekognition...).
- Python development experience and good practices.
- Knowledge of libraries such as pytorch and opencv.
Benefits & conditions
Competitive salary Commission Attractive benefits Tailored trainings and development opportunities International work environment, Competitive salary, commission and attractive benefits
Global career path for specialists and leadership
Tailored trainings and development opportunities
International and inspirational working environment with a dynamic work culture
Social Impact driven: enabling access to quality education through online learning, which is linked to the Sustainable Development Goals (SDG) 4. Access to Quality Education and 13. CO2 reduction. How do we measure it?
Objective 1: Strengthen the quality of online training. Indicator 1: number of online training users who have validated a professional degree or certification.
Objective 2: Improve accessibility to higher education or corporate training
Indicator 1: Number of users with disabilities who have been able to examine themselves remotely.
Indicator 2: Number of users in areas far from the training centers who have been able to examine themselves remotely.
Indicator 3: Number of users with time restrictions who have been able to examine themselves remotely.
Indicator 4: Number of users with financial restrictions who have been able to examine themselves remotely.