Middle ML Engineer (Computer Vision)
This position focuses on automating driver verification using computer vision techniques, requiring hands‑on experience building and deploying ML models. The engineer will also handle data preparation, annotation pipelines, and ensure security compliance across the system.
Skills required
About this role
Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle Machine Learning Engineer, who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in computer vision is essential.
Key Responsibilities
- Develop, deploy, and maintain machine learning models and systems
- Conduct in-depth data analysis, preparation, and processing
- Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing
- Follow established security guidelines and identify potential risks
- Collaborate with peers to review and ensure designs are effective, flexible, and reusable
- Solve issues and provide feedback for engineering design and delivery processes
- Ensure that features are deployed according to requirements and operate without unintended side effects
- Create and maintain comprehensive documentation for models and processes
- Monitor and support deployed solutions to ensure they meet performance expectations
- Actively seek out information and internal solutions to ensure reusability and adoption of common technologies
Our expectations
- 3+ years of experience in the Field. Previous software engineering experience is preferrable
- Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
- Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
- Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
- Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
- The ability to design solutions using common design patterns and various design tools.
- Experience in experiment design and the ability to validate the credibility of information from various sources.
- A solid understanding of the business value of delivered features.
- Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
- Excellent communication and collaboration skills, including active listening and the ability to build consensus.
- A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
- Ability to balance quality and speed depending on the case, and justify your decision
- Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
- English communication skills
