Software Engineering SMTS (AI/ML)

TL;DR

Software Engineering SMTS (AI/ML): Building and optimizing high-scale distributed machine learning and AI products on the Einstein platform, with an accent on Generative AI and Predictive AI applications, modern containerized deployment, and microservices architecture. Focus on designing, developing, debugging, and operating resilient distributed systems and driving engineering excellence for machine learning APIs.

Location: Onsite in Washington (Bellevue, Seattle) or California (Palo Alto, San Francisco, Los Angeles)

Company

hirify.global develops the Einstein platform, which democratizes AI and transforms how machine learning and AI products are built, providing unified, configuration-driven APIs and microservices for the entire ML lifecycle.

What you will do

• Work with large-scale distributed Machine Learning technologies like Sagemaker, Tensorflow, Pytorch, and Spark on modern containerized deployment stacks.
• Design, develop, debug, and operate resilient distributed systems across thousands of compute nodes in multiple datacenters.
• Own the technology for fully orchestrated machine learning APIs for the Einstein Platform.
• Partner with Product Managers, Architects, and Data Scientists to translate customer requirements into working software.
• Contribute to the long-range plan and drive microservices architectures for machine learning.
• Participate in the team’s on-call rotation to keep services operational and highly available.

Requirements

• BS, MS, or PhD in computer science or a related field, or equivalent work experience.
• 5+ years of hands-on experience with big data, machine learning, and microservices architectures.
• Track record of leading highly impactful projects from conception to finish.
• Expertise in JVM based languages (Java, Scala) and Python.
• Experience leading/working in teams that have built and run machine learning services at scale for predictive and generative models.
• Experience in building software on AWS cloud computing such as OpenSearch, DynamoDB, EMR, and S3.

Nice to have

• Experience working with Amazon SageMaker and Google Cloud ML.
• Experience building or leading teams that have built and run real-time data applications in production.