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Computer Vision Expert
2 weeks ago
A seasoned professional with a proven track record in architecting and deploying complex computer vision systems is sought after by our organization.
We are looking for an expert who can drive innovation and growth in the field of computer vision while maintaining high engineering standards.
- Sr. Computer Vision Engineer
The ideal candidate will have expertise in multi-object tracking, object detection, semi/unsupervised learning and proficiency in Python, PyTorch/TensorFlow, Modern C++, CUDA.
In addition to leading the design and implementation of complex CV pipelines with a focus on low latency and high reliability, the successful candidate will also be responsible for owning major modules from concept to deployment.
The role requires strong systems-level design thinking across ML lifecycles and experience with real-time, low-latency model deployment on edge devices.
Familiarity with MLOps (CI/CD for models, versioning, experiment tracking) is highly desirable as well as a Bachelor's/Master's degree in CS, EE, or related fields with strong ML and algorithmic foundations.
Key Responsibilities:
- Design and implement complex CV pipelines with a focus on low latency and high reliability.
- Owning major modules from concept to deployment ensuring seamless integration with existing systems.
- Transitioning algorithms from Python/PyTorch to optimized C++ edge GPU implementations using TensorRT, ONNX, and GStreamer.
- Collaborating with cross-functional teams to refine technical strategies and roadmaps.
- Driving long-term data and model strategies (synthetic data generation, validation frameworks).
- Mentoring engineers and maintaining high engineering standards.
To be considered for this position, candidates must possess 6-10 years of experience in architecting and deploying CV systems.
Desirable Skills and Qualifications:
- Expertise in multi-object tracking, object detection, semi/unsupervised learning.
- Proficiency in Python, PyTorch/TensorFlow, Modern C++, CUDA.
- Experience with real-time, low-latency model deployment on edge devices.
- Strong systems-level design thinking across ML lifecycles.
- Familiarity with MLOps (CI/CD for models, versioning, experiment tracking).
- Bachelor's/Master's degree in CS, EE, or related fields with strong ML and algorithmic foundations.
- (Preferred) Experience with NVIDIA DeepStream, GStreamer, LLMs/VLMs, open-source contributions.
