Computer Vision Engineer (Mid-Level)

About the Role

We are an Applied AI team bridging the gap between cutting-edge research and production systems. We are looking for an engineer who excels in AI model design, optimization, and real-world deployment.

In this role, you will architect solutions for complex visual understanding tasks. Your work will span robust object detection, instance segmentation, multi-object tracking, and multimodal reasoning with VLMs. Leveraging both classical computer vision and modern deep learning, you will own the full lifecycle of the model—from mathematical formulation to high-performance implementation in Python and C++.

Who We Are Looking For

We are primarily targeting Mid-level engineers. However, we are open to exceptional Junior candidates who possess a strong track record of relevant internships or significant undergraduate academic research.

Requirements

Key Qualifications

Education

• B.S. in Computer Science, Software Engineering, Electronics Engineering, or a related field.
• Preferred: M.S. in Computer Vision, Machine Learning, or a related area.

Experience:

• Mid: 3–5 years of hands-on experience developing and deploying CV/ML models.
• Junior: Must demonstrate solid internship experience or participation in rigorous academic studies/labs during undergrad.

Technical Stack:

• Advanced proficiency in Python and C++.
• Strong experience with deep learning frameworks (PyTorch or TensorFlow).
• Proficiency in debugging, profiling, and optimizing high-performance AI systems.
• We prioritize strong theoretical knowledge of deep learning and mathematical foundations over mere framework familiarity.

Domain Expertise:

• Deep understanding of object detection, segmentation, and object tracking.
• Familiarity with generative models (GANs, Diffusion models) and multimodal architectures.
• Foundational Knowledge:
• Strong grasp of linear algebra, probability, optimization, and numerical methods.

Bonus Qualifications

• Experience deploying models on edge devices or embedded systems.
• Knowledge of secure and privacy-aware AI practices.
• Hands-on experience with MLOps, CI/CD, and automated training pipelines.
• A track record of academic publications (CVPR, ICCV, ECCV, etc.) or significant open-source contributions.

Why Join Us ?

• Real-World Impact: Work on applied research that directly enhances safety and efficiency in the physical world.
• Collaborative Culture: Solve hard problems alongside a high-caliber, interdisciplinary team of engineers and researchers.
• Academic & Global Growth: We actively endorse academic publications and offer opportunities to work on global projects.
• Continuous Learning: We invest in your growth with sponsored access to premium online learning platforms (MOOCs) and internal technical workshops.
• Compensation: We offer a competitive salary package along with a comprehensive set of side benefits.