Machine Vision Engineer

Job Description : Machine Vision Engineer

Department : Machine Vision / Computer Vision

Location : Chennai

Type : Full-time

Experience : Mid senior to Senior (3–10+ years)

Location : Sriperumbudur, Chennai

Key Responsibilities

Algorithm Development : Design, implement, and optimize computer vision and deep learning algorithms for object detection, segmentation, 3D reconstruction, OCR, anomaly detection, and pose estimation.

End-to-End Pipeline Ownership : Develop and maintain robust vision pipelines, encompassing data ingestion, preprocessing, inference, post-processing, and system integration.

Model Lifecycle Management : Train, fine-tune, evaluate, and deploy deep learning models (CNNs, Transformers, etc.) using frameworks like PyTorch, TensorFlow, or JAX.

Data Strategy : Lead dataset curation, annotation, and augmentation efforts, including the use of synthetic data generation to improve model robustness.

Testing & Validation : Conduct rigorous testing, benchmarking, and error analysis under real-world conditions to ensure system reliability and performance.

Performance Optimization : Optimize models for edge deployment using techniques such as quantization, pruning, and leveraging inference engines like TensorRT, ONNX, or OpenVINO to meet strict latency and power constraints.

Embedded Integration : Collaborate with embedded engineers to port and optimize vision software for various hardware platforms (SoCs, GPUs, NPUs).

Research & Innovation : Stay current with the latest research in computer vision and machine learning, rapidly prototyping new techniques to enhance our capabilities.

Software Engineering Best Practices : Write clean, documented, and maintainable code, and actively contribute to internal libraries, tools, and code reviews.

Required Qualifications

Bachelor’s / master’s in computer science, Electrical Engineering, Robotics, or a related field.

3+ years of hands-on industry experience in computer vision or machine learning (including relevant internships).

Strong programming proficiency in Python and C++ .

Deep, practical experience with at least one major deep learning framework ( PyTorch is strongly preferred ).

A proven track record of deploying real-time computer vision systems into a production environment.

Solid foundation in classic computer vision (OpenCV, camera calibration, feature detection, multi-view geometry).

Experience working with 2D / 3D data formats, point clouds, and sensor fusion (e.g., camera with LiDAR / IMU).

Preferred Skills & Experience

Publications or significant contributions at top-tier conferences (CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS).

Experience with large-scale distributed model training.

Familiarity with MLOps tools and practices (e.g., Docker, Kubernetes, MLflow, Weights & Biases).

Domain experience in robotics, autonomous systems, medical imaging, or industrial quality inspection.

Knowledge of safety-critical software standards (e.g., IEC 61508, ISO 26262).

Strong mathematical background in linear algebra, probability, optimization, and 3D geometry.