318 Autonomous Systems jobs in Bahrain
AI/ML Engineer - Autonomous Systems
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Responsibilities:
- Design, develop, and implement AI/ML models for autonomous systems.
- Collect, clean, and preprocess large datasets for model training.
- Build and train predictive models using deep learning frameworks.
- Evaluate and optimize model performance for accuracy and efficiency.
- Deploy AI/ML solutions into real-world applications.
- Conduct research into new AI/ML techniques and technologies.
- Collaborate with cross-functional teams on system integration.
- Write clean, efficient, and well-documented code.
Qualifications:
- Master's or Ph.D. in Computer Science, Artificial Intelligence, or a related field.
- 3+ years of experience in AI/ML engineering.
- Proficiency in Python and/or C++.
- Experience with deep learning frameworks (TensorFlow, PyTorch, Keras).
- Knowledge of computer vision and machine learning algorithms.
- Experience with autonomous systems or robotics is a strong plus.
- Strong analytical and problem-solving skills.
- Excellent communication and teamwork abilities.
Senior Automotive Software Engineer - Autonomous Systems
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Key Responsibilities:
- Design, develop, and test advanced software modules for autonomous driving features, including sensor fusion, path planning, and decision-making algorithms.
- Implement and optimize real-time software for embedded automotive systems.
- Collaborate with cross-functional teams, including hardware engineers, AI researchers, and testing specialists.
- Develop and maintain simulation environments for autonomous vehicle software validation.
- Ensure software quality and reliability through rigorous testing, code reviews, and adherence to automotive industry standards (e.g., AUTOSAR, ISO 26262).
- Contribute to the architecture and design of the autonomous driving software stack.
- Troubleshoot and resolve complex software issues in a dynamic development environment.
- Stay current with the latest advancements in autonomous driving technology, AI, and machine learning.
- Mentor junior engineers and share technical expertise within the team.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field.
- Significant experience (5+ years) in software development for automotive systems, with a strong focus on autonomous driving or ADAS.
- Proficiency in C++ and Python, with a deep understanding of software development best practices.
- Experience with ROS (Robot Operating System) and other robotics middleware.
- Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and their application in autonomous systems.
- Experience with sensor technologies (LiDAR, radar, cameras) and data processing.
- Familiarity with simulation tools (e.g., CARLA, NVIDIA Drive Sim).
- Understanding of real-time operating systems (RTOS) and embedded systems development.
- Strong problem-solving and debugging skills.
- Excellent communication and teamwork abilities.
Machine Learning Engineer
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Responsibilities:
- Develop, train, and deploy machine learning models using various algorithms and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Build and maintain scalable machine learning pipelines for data processing, feature engineering, and model evaluation.
- Collaborate with data scientists and software engineers to integrate ML models into production systems.
- Monitor model performance in production and implement strategies for continuous improvement and retraining.
- Perform data analysis and feature engineering to enhance model accuracy.
- Stay up-to-date with the latest advancements in machine learning and artificial intelligence.
- Design and implement experiments to test hypotheses and evaluate model performance.
- Ensure the responsible and ethical use of AI and machine learning technologies.
- Optimize ML models for performance, scalability, and efficiency.
- Communicate complex technical concepts to both technical and non-technical stakeholders.
- Troubleshoot and resolve issues related to ML model deployment and performance.
- Contribute to the documentation of ML models, pipelines, and processes.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field.
- Minimum of 4 years of experience in machine learning engineering or a related role.
- Strong programming skills in Python and experience with ML libraries.
- Proven experience in building and deploying machine learning models in production.
- Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
- Solid understanding of data structures, algorithms, and software engineering principles.
- Experience with SQL and NoSQL databases.
- Excellent analytical, problem-solving, and critical thinking skills.
- Strong communication and collaboration skills.
- Ability to work effectively in a hybrid work environment.
Machine Learning Engineer
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Key Responsibilities:
- Design, develop, and implement machine learning models and algorithms.
- Preprocess, clean, and engineer features from large, complex datasets.
- Train, evaluate, and optimize ML models for performance and accuracy.
- Deploy ML models into production environments using MLOps practices.
- Collaborate with data scientists and engineers to integrate ML solutions.
- Research and apply the latest AI and ML techniques and technologies.
- Develop and maintain documentation for ML models and pipelines.
- Monitor model performance in production and retrain as needed.
- Contribute to the development of AI strategy and roadmap.
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field.
- 3+ years of experience in machine learning engineering or data science with a focus on applied ML.
- Strong proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
- Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools.
- Solid understanding of statistical modeling, data mining, and machine learning algorithms.
- Excellent problem-solving and analytical skills.
- Strong communication and collaboration abilities.
Machine Learning Engineer
Posted today
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Job Description
Responsibilities:
- Design, develop, and implement machine learning models and algorithms.
- Perform data preprocessing, feature engineering, and data analysis to prepare datasets for model training.
- Train, evaluate, and optimize machine learning models for performance and accuracy.
- Deploy machine learning models into production environments.
- Collaborate with data scientists and software engineers to integrate ML solutions into existing systems.
- Stay current with the latest advancements in AI and machine learning research and techniques.
- Develop and maintain robust MLOps pipelines for model deployment and monitoring.
- Conduct experiments and research to explore new ML approaches.
- Write clean, efficient, and well-documented code.
- Communicate technical findings and model performance to stakeholders.
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field.
- Minimum of 4-6 years of experience in machine learning engineering or data science.
- Strong proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Solid understanding of statistical modeling, algorithms, and machine learning concepts.
- Experience with cloud platforms (AWS, Azure, GCP) and MLOps practices.
- Proven experience in data preprocessing, feature engineering, and model evaluation.
- Excellent problem-solving and analytical skills.
- Strong communication and collaboration abilities.
- Experience with big data technologies (e.g., Spark) is a plus.
- Published research in reputable ML conferences or journals is a plus.
Machine Learning Engineer
Posted today
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Job Description
Key Responsibilities:
- Design, develop, train, and deploy machine learning models and algorithms.
- Implement and manage data pipelines for model training and evaluation.
- Collaborate with data scientists and software engineers to integrate ML models into production systems.
- Optimize model performance for accuracy, scalability, and efficiency.
- Stay up-to-date with the latest advancements in machine learning, deep learning, and AI research.
- Develop robust testing and validation strategies for ML models.
- Monitor deployed models for performance drift and implement retraining strategies.
- Work with large datasets, performing feature engineering and selection.
- Contribute to the architectural design of ML platforms and infrastructure.
- Document model development processes, findings, and deployment procedures.
Qualifications:
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field.
- Minimum of 5 years of experience in machine learning engineering or data science with a focus on model development and deployment.
- Proficiency in programming languages such as Python (with libraries like TensorFlow, PyTorch, Scikit-learn, Pandas).
- Experience with cloud platforms (AWS, Azure, GCP) and their ML services.
- Solid understanding of various ML algorithms, including supervised, unsupervised, and deep learning techniques.
- Experience with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow).
- Strong analytical, problem-solving, and critical thinking skills.
- Excellent communication and collaboration abilities.
- Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
- Ability to work effectively in a fast-paced, dynamic environment.
This position offers a highly competitive salary, comprehensive benefits package, and the opportunity to work on groundbreaking AI projects.
Machine Learning Engineer
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Key responsibilities include developing robust and scalable machine learning pipelines, from data preprocessing and feature engineering to model evaluation and deployment. You will collaborate with data scientists and software engineers to implement and optimize ML algorithms. Ensuring the performance, accuracy, and reliability of deployed models will be a core duty. You will also be responsible for monitoring model performance in production, identifying drift, and implementing retraining strategies. Contributing to the design and development of ML infrastructure and tooling is expected. Staying current with state-of-the-art ML techniques and contributing to the team's knowledge base are essential. You will also participate in code reviews and contribute to documentation.
The ideal candidate will hold a Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field. Proven experience in building and deploying machine learning models in a production environment is required. Proficiency in programming languages such as Python, and experience with ML libraries and frameworks like Scikit-learn, TensorFlow, or PyTorch, are mandatory. Strong understanding of data structures, algorithms, and software engineering best practices is essential. Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes) is highly desirable. Excellent analytical, problem-solving, and communication skills are crucial. Join our innovative team and help us build the next generation of AI-powered products.
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Senior Machine Learning Engineer
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Senior Machine Learning Engineer
Posted today
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Job Description
Our client, a cutting-edge technology company specializing in AI-driven solutions, is seeking a Senior Machine Learning Engineer to join their fully remote, globally distributed team. This role is pivotal in developing and deploying advanced machine learning models that power our client's innovative products and services. You will work on challenging problems, leveraging large datasets to build, train, and optimize predictive models. As a Senior ML Engineer, you will be responsible for the end-to-end machine learning pipeline, from data preprocessing and feature engineering to model deployment and monitoring. Key responsibilities include researching and implementing state-of-the-art ML algorithms, developing robust and scalable ML systems, and collaborating with data scientists and software engineers to integrate ML capabilities into production environments. The ideal candidate will have extensive experience in machine learning, deep learning, and MLOps, with a strong foundation in software engineering principles. Proficiency in Python, along with deep experience in ML frameworks like TensorFlow, PyTorch, or scikit-learn, is essential. Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes) is highly valued. A Master's or PhD in Computer Science, Artificial Intelligence, or a related quantitative field is preferred. Excellent problem-solving skills, a passion for innovation, and the ability to work independently and communicate effectively in a remote setting are critical. Join our client and contribute to building intelligent systems that are transforming industries.
Responsibilities:
- Design, build, and maintain scalable machine learning systems.
- Develop and implement advanced ML models and algorithms.
- Perform data preprocessing, feature engineering, and model evaluation.
- Deploy ML models into production environments (MLOps).
- Collaborate with data scientists and software engineers.
- Optimize ML models for performance and efficiency.
- Monitor deployed models and retrain as necessary.
- Research and apply new ML techniques and technologies.
- Contribute to the company's AI strategy and roadmap.
- Master's or PhD in Computer Science, AI, or a related field.
- Extensive experience in Machine Learning and Deep Learning.
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
- Experience with MLOps practices and tools.
- Strong software engineering skills.
- Familiarity with cloud platforms (AWS, Azure, GCP).
- Knowledge of containerization (Docker, Kubernetes).
- Excellent analytical and problem-solving abilities.
- Proven ability to work independently in a remote team.
Senior Machine Learning Engineer
Posted today
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Job Description
Key Responsibilities:
- Design, develop, and implement machine learning models and algorithms.
- Process and analyze large datasets to extract meaningful insights.
- Collaborate with data scientists and software engineers to integrate ML models into production systems.
- Build and maintain data pipelines for training and deploying ML models.
- Evaluate and improve the performance and scalability of ML models.
- Stay current with the latest research and advancements in machine learning and AI.
- Develop robust testing and validation strategies for ML models.
- Contribute to the development of MLOps practices and tools.
- Mentor junior engineers and share knowledge within the team.
- Troubleshoot and resolve issues related to ML model performance and deployment.
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field.
- Minimum of 5 years of experience in machine learning engineering or a related role.
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong understanding of various ML algorithms, including supervised, unsupervised, and deep learning.
- Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools.
- Excellent analytical, problem-solving, and communication skills.
- Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
- Ability to work independently and collaboratively in a remote team environment.