Computer Vision ML Engineer Job at Apple।New Opportunity

Job Role : Computer Vision ML Engineer

Salary : INR 7,50,000 per year. 

Company : Apple

Location : Bengaluru, Karnataka

Qualifications : bachelor’s or master’s diploma

Experience : 3+ years

ABOUT COMPANY

Apple Inc. Is a multinational era agency established in Cupertino, California, USA. Founded in 1976 by using Steve Jobs, Steve Wozniak, and Ronald Wayne, Apple designs, develops, and sells purchaser electronics, pc software, and on-line offerings.Its iconic products include the iPhone, iPad, Mac computers, Apple Watch, and Apple TV, in addition to software like iOS, macOS, watchOS, and tvOS. Apple is understood for its innovation, glossy design, and dedication to consumer privateness and protection.It operates retail stores international and has a giant presence in the digital media marketplace with offerings like the App Store, iTunes, and Apple Music.

Job Description

As a Computer Vision ML Engineer, you may expand algorithms and models to analyze and interpret visual data. Responsibilities include designing and enforcing computer imaginative and prescient systems, optimizing algorithms for overall performance and accuracy, and taking part with pass-purposeful groups to combine imaginative and prescient answers into products. You’ll research and follow current techniques in deep learning, photo processing, and pattern recognition to remedy complicated issues. Strong programming capabilities in languages like Python and proficiency with libraries which include TensorFlow or PyTorch are critical. Additionally, you will need know-how in image processing techniques, familiarity with information augmentation strategies, and a solid know-how of gadget gaining knowledge of principles.

Requirements of Computer Vision ML Engineer:

• Education: Typically, a bachelor’s or master’s diploma in pc technology, electric engineering, mathematics, or a associated subject is needed. A Ph.D. Is often favored for greater superior roles.

• Experience: Previous revel in in computer vision, system studying, or a associated field is critical. Entry-degree positions may additionally require internships or relevant educational tasks, while senior roles often call for several years of enterprise experience.

• Programming Skills: Proficiency in programming languages generally used in machine mastering and pc vision is a must. This often includes Python, along with libraries such as TensorFlow, PyTorch, OpenCV, and scikit- study.(Computer Vision ML Engineer)

• Mathematics and Statistics: A robust understanding of mathematical concepts, consisting of linear algebra, calculus, opportunity, and facts, is important for developing and optimizing device mastering algorithms.

• Problem-Solving Skills: Computer vision ML engineers must own incredible problem-fixing competencies, as they frequently come across complicated demanding situations while studying and interpreting visual facts.

Responsibilities of Computer Vision ML Engineer:

• Algorithm Development: Develop and enforce algorithms and models for studying visual records, which include object detection, picture segmentation, and characteristic extraction.
• Model Training and Evaluation: Train system gaining knowledge of models the usage of categorized datasets and evaluate their overall performance using appropriate metrics. Iterate on models to enhance accuracy, efficiency, and robustness.
• Optimization: Optimize algorithms and fashions for overall performance, scalability, and computational efficiency. This may additionally contain strategies such as pruning, quantization, and version compression.
• Research: Stay up to date with the cutting-edge improvements in pc vision and gadget getting to know research. Apply current techniques to remedy real-world problems and push the bounds of what’s feasible.(Computer Vision ML Engineer)
• Collaboration: Collaborate with pass-practical groups, such as software program engineers, facts scientists, and product managers, to combine laptop imaginative and prescient answers into products and services.
• Documentation: Document algorithms, models, experiments, and outcomes in a clean and concise manner. This consists of writing technical reports, studies papers, and documentation for internal and external stakeholders.
• Testing and Validation: Design and execute checks to validate the performance and reliability of pc imaginative and prescient systems. This might also involve unit trying out, integration testing, and real-global validation in diverse environments.

Skills of Computer Vision ML Engineer:

1. Deep Learning: Strong expertise of deep getting to know standards and strategies, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
2. Image Processing: Proficiency in picture processing strategies, along with filtering, aspect detection, morphological operations, and photo enhancement.
3. Feature Extraction: Knowledge of feature extraction methods, consisting of handcrafted capabilities and discovered representations, for tasks such as item popularity and photo type.
4. Data Augmentation: Familiarity with statistics augmentation strategies to boom the diversity and size of schooling datasets, along with rotation, scaling, translation, and flipping.
5. Software Development: Strong software development talents, which includes model manage, debugging, and code optimization. Experience with software engineering first-class practices, such as modular design and code reusability.(Computer Vision ML Engineer)
6. Communication: Excellent conversation capabilities, each written and verbal, to correctly speak complicated technical concepts to diverse audiences. This consists of presenting findings, taking part with teammates, and documenting work.
7. Problem-Solving: Strong hassle-fixing skills and a creative mindset to tackle difficult troubles in pc imaginative and prescient and system mastering. The capability to think critically, experiment with one of a kind methods, and iterate on solutions is critical.(Computer Vision ML Engineer)
8. Domain Knowledge: Depending at the utility area, domain-precise knowledge can be required. For instance, revel in in fields inclusive of robotics, independent motors, healthcare, or retail can be beneficial for know-how precise demanding situations and necessities.

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