Senior Machine Learning Engineer

About the Role A Senior Machine Learning Engineer is an accomplished expert in designing and deploying advanced machine learning solutions. They possess deep expertise in algorithms, programming languages like Python, and specialized frameworks. This role involves leading complex machine-learning projects, making critical architectural decisions, optimizing performance models, and collaborating with interdisciplinary teams. Senior Machine Learning Engineers contribute to cutting-edge research, develop innovative solutions, and stay at the forefront of the field to deliver impactful and state-of-the-art machine learning applications. Responsibilities Designing and programming machine learning methods and integrating them into our ML framework/pipeline. Evaluating and validating the analysis with statistical methods. Also presenting this in a lucid form to people not familiar with the domain of data science/computer science. Provide mentorship and guidance to junior machine learning engineers, to review and improve their code, ensuring adherence to coding standards, performance optimization, and maintainability. Helping junior team members enhance their technical skills and overall professional development. Stay up-to-date with the latest advancements and trends in machine learning, deep learning, and related fields. Conduct research and explore new technologies, frameworks, and tools to improve our machine-learning models and processes. Collaboratively design and implement machine learning systems and infrastructure, leveraging your experience to make informed decisions and drive successful outcomes. Requirements B.E.\B. Tech\M.E.\M.S.\M. Tech\PhD preferably in fields related to Computer Science with experience in machine learning, image, and signal processing, or statistics preferred. Background and knowledge of recent advances in machine learning, deep learning, natural language processing, and/or image/signal/video processing with 3+ years of professional work experience working on real-world data. Strong programming background, and knowledge of software engineering concepts (OOP, design patterns). Knowledge of machine learning libraries TensorFlow, Jax, Keras, Scikit-learn, PyTorch. Excellent mathematical skills and background, e.g. accuracy, significance tests, advanced probability concepts, linear algebra. Ability to perform both independent and collaborative research. Excellent written and spoken communication skills. A proven ability to work in a cross-discipline environment in defined time frames. Knowledge and experience in deploying large-scale systems using distributed and cloud-based systems is a big plus. Show more Show less