3 Ultralytics Yolo Jobs

As a Machine Learning Engineer/Data Scientist, you will be responsible for developing, coding, and deploying advanced ML models with expertise in computer vision, object detection, object tracking, and NLP. Your primary responsibilities will include designing, testing, and deploying machine learning pipelines, developing computer vision algorithms using tools like Ultralytics YOLO and OpenCV, as well as building and optimizing NLP models for text analysis and sentiment analysis. You will also be utilizing Google Cloud services for model development and deployment, writing clean and efficient code, collaborating with cross-functional teams, and staying updated with the latest advancements in machine learning and AI. You should possess a Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field, along with at least 5 years of professional experience in data science and machine learning. Advanced proficiency in Python, TensorFlow, PyTorch, OpenCV, and Ultralytics YOLO is required, as well as hands-on experience in developing computer vision models and NLP solutions. Familiarity with cloud platforms, strong analytical skills, and excellent communication abilities are essential for this role. Preferred skills include experience with MLOps practices, CI/CD pipelines, big data technologies, distributed computing frameworks, contributions to open-source projects, and expertise in model interpretability, monitoring, and performance tuning in production environments. If you are passionate about machine learning, enjoy solving complex business challenges, and have a knack for developing cutting-edge models, this role is perfect for you.,

We are seeking a highly skilled, hands-on Machine Learning Engineer/Data Scientist with a robust background in developing, coding, and deploying advanced ML models. The ideal candidate will have demonstrable expertise in computer vision, object detection, object tracking, and NLP, with a proven track record of writing production-grade code and developing state-of-the-art models to solve complex business challenges. Key Responsibilities: End-to-End ML Pipeline Development: Design, code, test, and deploy robust machine learning pipelinesfrom data collection and preprocessing to model training and production deployment. Computer Vision & Object Detection: Develop and fine-tune algorithms for computer vision applications using tools like Ultralytics YOLO and OpenCV, with hands-on experience in object detection and object tracking. NLP Model Development: Build and optimize NLP models for tasks such as text analysis, sentiment analysis, and language processing using frameworks like Hugging Face Transformers or spaCy. Cloud-Based Model Deployment: Utilize Google Cloud services, particularly Vertex AI, for scalable model development, training, testing, and deployment. Code Quality & Optimization: Write clean, modular, and efficient code; perform debugging, code reviews, and performance tuning to ensure scalable and maintainable codebases. Collaboration & Integration: Work closely with data engineering and software development teams to integrate ML models into production environments and ensure seamless deployment. Continuous Improvement: Stay up-to-date with the latest advancements in machine learning and AI, integrating new technologies and methods as appropriate. Documentation & Best Practices: Maintain thorough documentation of code, methodologies, and experiments, adhering to industry best practices and MLOps principles. Required Skills and Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. 5+ years of professional experience in data science and machine learning with a strong emphasis on hands-on coding and model development. Advanced proficiency in Python, and extensive experience with ML libraries and frameworks such as TensorFlow, PyTorch, OpenCV, and Ultralytics YOLO. Demonstrable expertise in developing and deploying computer vision models (object detection and tracking) and NLP solutions. Hands-on experience with cloud platforms, especially Google Cloud (Vertex AI), for scalable ML model training and deployment. Strong analytical, debugging, and performance optimization skills, with a commitment to high-quality code and software engineering best practices. Familiarity with data annotation platforms (e.g., Roboflow) and experience in integrating these into model development workflows. Excellent communication and collaboration skills to work effectively in cross-functional teams. Preferred Skills: Experience with MLOps practices and CI/CD pipelines for machine learning. Familiarity with big data technologies and distributed computing frameworks. Contributions to open-source projects or publications in relevant fields. Expertise in model interpretability, monitoring, and performance tuning in production environments. (Hands-On Coding & Model Development)

We are seeking a highly skilled, hands-on Machine Learning Engineer/Data Scientist with a robust background in developing, coding, and deploying advanced ML models. The ideal candidate will have demonstrable expertise in computer vision, object detection, object tracking, and NLP, with a proven track record of writing production-grade code and developing state-of-the-art models to solve complex business challenges. Key Responsibilities: End-to-End ML Pipeline Development: Design, code, test, and deploy robust machine learning pipelinesfrom data collection and preprocessing to model training and production deployment. Computer Vision & Object Detection: Develop and fine-tune algorithms for computer vision applications using tools like Ultralytics YOLO and OpenCV, with hands-on experience in object detection and object tracking. NLP Model Development: Build and optimize NLP models for tasks such as text analysis, sentiment analysis, and language processing using frameworks like Hugging Face Transformers or spaCy. Cloud-Based Model Deployment: Utilize Google Cloud services, particularly Vertex AI, for scalable model development, training, testing, and deployment. Code Quality & Optimization: Write clean, modular, and efficient code; perform debugging, code reviews, and performance tuning to ensure scalable and maintainable codebases. Collaboration & Integration: Work closely with data engineering and software development teams to integrate ML models into production environments and ensure seamless deployment. Continuous Improvement: Stay up-to-date with the latest advancements in machine learning and AI, integrating new technologies and methods as appropriate. Documentation & Best Practices: Maintain thorough documentation of code, methodologies, and experiments, adhering to industry best practices and MLOps principles.Required Skills and Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. 5+ years of professional experience in data science and machine learning with a strong emphasis on hands-on coding and model development. Advanced proficiency in Python, and extensive experience with ML libraries and frameworks such as TensorFlow, PyTorch, OpenCV, and Ultralytics YOLO. Demonstrable expertise in developing and deploying computer vision models (object detection and tracking) and NLP solutions. Hands-on experience with cloud platforms, especially Google Cloud (Vertex AI), for scalable ML model training and deployment. Strong analytical, debugging, and performance optimization skills, with a commitment to high-quality code and software engineering best practices. Familiarity with data annotation platforms (e.g., Roboflow) and experience in integrating these into model development workflows. Excellent communication and collaboration skills to work effectively in cross-functional teams. Preferred Skills: Experience with MLOps practices and CI/CD pipelines for machine learning. Familiarity with big data technologies and distributed computing frameworks. Contributions to open-source projects or publications in relevant fields. Expertise in model interpretability, monitoring, and performance tuning in production environments.