Job Summary

Startup Inno is seeking an innovative and detail-oriented Data Scientist specializing in Computer Vision to join our Autonomous Systems team. In this role, you will design, develop, and deploy advanced vision-based machine learning models that power next-generation autonomous technologies. You will work closely with cross-functional teams including robotics, software engineering, and product management to transform complex visual data into actionable intelligence.

This is an exciting opportunity for a technically strong professional who is passionate about artificial intelligence, real-time perception, and building scalable solutions for autonomous environments.

Key Responsibilities

• Design, train, and optimize computer vision and deep learning models for autonomous systems.

• Develop perception pipelines for object detection, tracking, segmentation, and scene understanding.

• Work with large-scale image and video datasets to improve model accuracy and robustness.

• Collaborate with robotics and embedded teams to deploy models into production environments.

• Conduct experiments, evaluate model performance, and iterate based on findings.

• Implement data preprocessing, augmentation, and annotation strategies.

• Stay current with the latest research in computer vision, deep learning, and autonomous technologies.

• Document methodologies, maintain code quality, and support knowledge sharing across teams.

Required Skills and Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Robotics, or a related field.

• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.

• Solid understanding of computer vision techniques (CNNs, transformers, image processing).

• Experience with OpenCV and image/video data pipelines.

• Knowledge of machine learning fundamentals and model evaluation metrics.

• Familiarity with version control systems such as Git.

• Strong analytical thinking and problem-solving skills.

• Excellent written and verbal communication abilities.

Experience

• 2–5 years of hands-on experience in computer vision or applied machine learning roles.

• Proven experience building and deploying vision models in real-world applications.

• Experience in autonomous driving, robotics, drones, or edge AI is highly preferred.

• Exposure to MLOps, model optimization, or real-time inference is a plus.

Working Hours

• Full-time position.

• Standard working hours with flexible scheduling options.

• Occasional overlap with global teams may be required.

• Hybrid or remote flexibility based on project needs.

Knowledge, Skills, and Abilities

• Deep understanding of deep learning architectures for visual perception.

• Ability to work with large-scale structured and unstructured datasets.

• Strong debugging and performance optimization skills.

• Capability to translate research papers into production-ready solutions.

• Collaborative mindset with the ability to work in agile environments.

• High attention to detail and commitment to quality delivery.

Benefits

• Competitive salary and performance-based bonuses.

• Health insurance and wellness programs.

• Flexible work arrangements and remote options.

• Professional development and learning support.

• Access to cutting-edge AI and autonomous systems projects.

• Collaborative and innovation-driven work culture.

• Paid time off and company holidays.

Why Join

At Startup Inno, you will work on impactful technologies shaping the future of autonomous systems. We foster a culture of innovation, ownership, and continuous learning. You will have the opportunity to solve real-world challenges, collaborate with top-tier talent, and contribute to products that push the boundaries of AI-driven perception.

How to Apply

Interested candidates should submit their updated resume, portfolio (if available), and a brief cover letter highlighting relevant computer vision experience. Applications will be reviewed on a rolling basis. Shortlisted candidates will be contacted for the next steps in the selection process.