Senior ML Platform Engineer [28496]

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Written by AI from the job title and company, not by Stealth, so some details may not match this job. The employer's own page has the real description.

Overview

The Senior ML Platform Engineer at Stealth will play a pivotal role in the design, development, and maintenance of a robust machine learning platform. This position involves collaborating with cross-functional teams to ensure the seamless integration of machine learning models into production environments. The ideal candidate will take charge of optimizing the machine learning lifecycle, including data ingestion, model training, and deployment processes, while also providing technical mentorship to junior team members.

Job Responsibilities

• Design, implement, and enhance scalable machine learning infrastructure and tools.
• Develop and maintain automated workflows for model training, evaluation, and deployment.
• Collaborate with data scientists to ensure effective model integration and optimization.
• Monitor and troubleshoot performance issues in the ML pipeline and production models.
• Implement best practices for version control, testing, and scalability of ML solutions.
• Provide mentorship and support to junior engineers within the team.
• Stay abreast of latest industry trends and advancements in machine learning technologies.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 5+ years of experience in software engineering and machine learning platforms.
• Proficient in languages such as Python, Java, or Scala.
• Strong background in cloud services (AWS, GCP, Azure) and distributed computing.
• Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of data engineering principles and data manipulation techniques.
• Excellent problem-solving skills and ability to work independently and in a team environment.

Technologies & Tools

In this role, you will utilize a variety of technologies and tools to enhance machine learning practices. Key technologies include cloud platforms such as AWS or Google Cloud Platform for deploying ML models, along with containerization tools like Docker and orchestration systems like Kubernetes. Additionally, expertise in machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and Java, will be essential for developing and maintaining the ML infrastructure.

Ideal Candidates

The ideal candidate for the Senior ML Platform Engineer position should exhibit strong analytical thinking and a passion for innovation in machine learning technologies. Being a proactive communicator with the ability to mentor others is vital, as is a commitment to continuous improvement and learning. Candidates who thrive in collaborative environments and can adapt to changing priorities while maintaining attention to detail will excel in this role.