Software Engineer-Machine Learning Infrastructure.

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Job Overview

The Software Engineer - Machine Learning Infrastructure at Stellar Cyber will play a critical role in the development and maintenance of robust machine learning systems that enhance our cybersecurity products. The ideal candidate will leverage their expertise in software engineering and machine learning to design and implement infrastructure that supports model training, evaluation, and deployment. By collaborating with data scientists, they will ensure that the infrastructure scales effectively and meets both performance and reliability requirements, driving innovation and efficiency across our machine learning initiatives.

Job Responsibilities

• Design and develop scalable machine learning infrastructure to support various models and datasets.
• Collaborate with cross-functional teams to gather requirements and optimize model deployment.
• Implement, maintain, and improve CI/CD pipelines for machine learning workflows.
• Monitor system performance and troubleshoot issues related to machine learning operations.
• Ensure data integrity and security in machine learning processes.
• Stay updated with the latest advancements in machine learning frameworks and tools.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree preferred.
• Strong experience in software development, particularly in Python and Java.
• Proficiency with machine learning frameworks such as TensorFlow, PyTorch, or equivalent.
• Experience with cloud services (e.g., AWS, GCP, or Azure) and containerization (e.g., Docker, Kubernetes).
• Solid understanding of data structures, algorithms, and software design principles.
• Familiarity with data warehousing solutions and large-scale data processing technologies.

Technologies & Tools

This position relies on various technologies and tools to facilitate innovative machine learning infrastructure. The candidate will work extensively with Python as the primary programming language, and utilize frameworks like TensorFlow and PyTorch for model development. Additionally, tools such as Docker and Kubernetes for container orchestration, as well as cloud platforms including AWS, GCP, or Azure for infrastructure deployment will be integral to the role.

Ideal Candidates

The ideal candidate for the Software Engineer - Machine Learning Infrastructure position is a proactive and analytical thinker who possesses a strong collaborative spirit. They should have a passion for leveraging machine learning to tackle complex cybersecurity challenges. Excellent problem-solving skills, a keen attention to detail, and the ability to communicate technical concepts clearly to non-technical stakeholders are essential. The candidate should also demonstrate a commitment to continuous learning and adapting to the fast-paced nature of technology advancements.