Nvidia Senior ML Engineer 

Israel, North District 

862057690

As a ML Engineer, you will play a significant role in bridging the gap between machine learning and software engineering. Your primary responsibility will be to develop, deploy, and maintain the infrastructure and workflows required for the end-to-end machine learning lifecycle. If you are genuinely passionate about machine learning operations and possesses excellent problem-solving, we'd love to hear from you!

• Collaborate with multi-functional teams, including data scientists, data engineers, and DevOps, to craft, implement, and maintain scalable ML infrastructure and workflows.
• Develop and implement deployment strategies for machine learning models, ensuring efficient and reliable model serving in manufacturing production environments.
• Automate and streamline the ML workflow, including data ingestion, feature engineering, model training, and model evaluation, using industry's gold standard methodologies, frameworks, and tools.
• Build and maintain scalable data pipelines, ensuring efficient data storage, retrieval, and transformation for ML use cases.
• Monitor and optimize ML systems for performance, scalability, and reliability.
• Implement and maintain CI/CD pipelines for ML projects, enabling continuous integration, deployment, and monitoring of ML models.

• Bachelor’s or master’s degree in computer science, Engineering, or a related field.
• 5+ years of experience as a ML Engineer or in a similar role, working on deploying and leading machine learning models in production environments.
• Strong programming skills in languages such as Python, Java, or Scala, with experience in building scalable and efficient systems.
• Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Solid knowledge of building and maintaining RESTful APIs, including creating scalable, secure, and efficient endpoints. Experience with backend frameworks like Flask, FastAPI, or Django, and familiarity with API documentation standards such as Sphinx/Swagger.

• Strong problem-solving and analytical skills, with the ability to handle complex technical challenges and provide innovative solutions.
• Demonstrated experience in designing and implementing computer vision solutions, including image and video processing, object detection, segmentation, and classification using tools and frameworks such as OpenCV, TensorFlow, PyTorch, or similar. Strong understanding of deep learning algorithms and their applications to real-world problems.
• Proficient in distributed computing frameworks such as Dask and Apache Spark, with hands-on experience using Databricks for scalable data processing and advanced analytics.
• Self-motivated with a goal to stay updated on the latest methodologies and machine learning operations technologies.