Machine Learning Engineer

Full time na a Laimoon Verified Company no Saudi Arabia

Publicado em November 11, 2024

Detalhes do emprego

We are seeking a talented and experienced Senior Machine Learning Engineer to join our dynamic team in Riyadh, Saudi Arabia. In this role, you will play a crucial part in shaping the future of brain-focused technology by designing, implementing, and optimizing machine learning algorithms and models that drive our innovative solutions. If you're passionate about pushing the boundaries of machine learning to enhance human cognitive capabilities, we'd love to hear from you.Key Responsibilities:Lead the development and deployment of advanced machine learning algorithms and models to analyze brain data and extract meaningful insights.Collaborate closely with cross-functional teams including data scientists, engineers, and neuroscientists to identify opportunities for applying machine learning techniques.Design, implement, and optimize machine learning pipelines for data preprocessing, feature extraction, and model training.Explore and experiment with cutting-edge machine learning methodologies to solve complex challenges in brain health and performance.Drive continuous improvement by researching and implementing state-of-the-art techniques in machine learning and artificial intelligence.Mentor and guide junior team members, fostering a collaborative and innovative environment.Collaborate with product teams to integrate machine learning solutions into Neuroline's technologies and applications.Stay up-to-date with the latest developments in machine learning and brain research, contributing to the company's thought leadership in the field.Key Skills:Strong proficiency in machine learning algorithms, data structures, and optimization techniques.Extensive experience in developing and deploying machine learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn.Expertise in deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models.Solid understanding of statistical concepts and data analysis techniques.Proficiency in programming languages, particularly Python, for prototyping and development.Ability to preprocess and manipulate diverse types of data, including time-series, image, and text data.Strong problem-solving skills and the ability to develop creative solutions to complex technical challenges.Experience in neural signal processing, EEG analysis, or neuroimaging is a significant advantage.Familiarity with cloud platforms and distributed computing for training large-scale models.Excellent communication skills, both written and verbal, to effectively collaborate with interdisciplinary teams.Proven experience in mentoring and guiding junior machine learning engineers.Qualifications:Master's or Ph.D. degree in Computer Science, Electrical Engineering, or a related field with a focus on machine learning.Proven track record of designing and implementing machine learning algorithms and models, ideally with applications in neuroscience, healthcare, or related fields.Eligibility to work in Saudi Arabia.Why Neuroline:Be part of a team that's shaping the future of brain-focused technology.Contribute to innovative solutions that enhance human cognitive potential.Collaborative and inclusive work environment that values diverse perspectives.Competitive compensation package and benefits.Professional growth opportunities in a rapidly evolving field.Application Process:If you're ready to drive innovation in machine learning and brain health, we invite you to submit your resume, a cover letter outlining your relevant experience, and any relevant work/portfolio examples to career@neuroline.ai.Please use the subject line "Senior Machine Learning Engineer Application - (Your Name)".Neuroline is an equal opportunity employer. We encourage applications from candidates of all backgrounds and experiences. Join us in pushing the boundaries of technology to unlock the potential of the human brain. Together, we can make a lasting impact on brain health and performance.

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