As a Machine Learning Engineer, you will be responsible for designing, building, and deploying machine learning models and systems. You will work closely with data scientists, software engineers, and domain experts to develop algorithms that can learn from vast datasets and solve complex real-world problems. You will focus on scalable model development, deployment, and optimization to ensure the models operate efficiently in production environments.

Key Responsibilities:

• Develop, test, and deploy machine learning models and algorithms to meet business and technical requirements.
• Collaborate with cross-functional teams including data scientists, software engineers, and product managers to integrate ML solutions into products and services.
• Work with large datasets to extract meaningful insights and features that contribute to model performance.
• Implement end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, validation, and deployment.
• Monitor model performance, troubleshoot issues, and continuously improve accuracy, speed, and efficiency.
• Stay updated with the latest advancements in machine learning and AI technologies and apply them to improve current systems.
• Optimize model scalability and performance for real-time or batch processing use cases.
• Conduct experiments and A/B testing to validate model improvements and measure business impact.
• Ensure ethical and responsible AI practices in the development and deployment of machine learning models.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field (Ph.D. is a plus).
• Strong programming skills in Python, R, or Java, with experience in machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
• Proven experience in designing and implementing machine learning algorithms (supervised and unsupervised learning, deep learning, reinforcement learning).
• Proficiency in handling large-scale data using tools like Hadoop, Spark, SQL, or NoSQL databases.
• Experience with cloud platforms like AWS, Google Cloud, or Azure for deploying machine learning models.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Experience with version control systems like Git and machine learning operations (MLOps) is a plus.
• Knowledge of model evaluation metrics, hyperparameter tuning, and model optimization techniques.
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications:

• Experience with Natural Language Processing (NLP), Computer Vision (CV), or other advanced AI applications.
• Familiarity with big data technologies and distributed computing environments.
• Understanding of AI ethics and privacy concerns in machine learning solutions.

Benefits:

• Competitive salary and performance-based bonuses.
• Health, dental, and vision insurance.
• Paid time off and flexible working hours.
• Opportunities for continuous learning and professional development.
• Collaborative work environment with cutting-edge technology projects.

How to Apply:
Interested candidates should submit their resume, cover letter, and any relevant work samples or portfolios to hr@cyberbeak.com. Please include “ML Engineer Application” in the subject line.