Machine Learning System Engineer (AI)

TL;DR

Machine Learning System Engineer (AI): Building and maintaining core infrastructure to enable machine learning engineers and data scientists to develop, train, evaluate, deploy, and operate Machine Learning models and pipelines with an accent on solving difficult infrastructure and architecture challenges. Focus on leading technical design, driving projects from technical design to launch, and collaborating with other teams to set expectations.

Location: Remote (Global), hiring in any country where hirify.global has a legal entity.

Company

hirify.global creates software products to unleash the potential of every team, facilitating collaboration and solving complex problems together.

What you will do

• Build and maintain core infrastructure for Machine Learning model development, training, evaluation, deployment, and operation.
• Apply software development expertise to solve complex infrastructure and architecture challenges.
• Lead other engineers to drive projects from technical design to launch.
• Collaborate with internal teams and customers to gather input and communicate results.
• Accelerate AI innovation and provide cohesive AI experiences across hirify.global products.
• Construct underlying AI infrastructure crucial for seamless integration and optimal functionality.

Requirements

• 3+ years of experience as a software developer.
• Fluency in at least one modern object-oriented programming language (Java/Kotlin and Python preferred).
• Experience with Continuous Delivery and Continuous Integration.
• Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking).
• Experience with distributed large-scale data processing (Apache Spark).
• Basic understanding of Machine Learning projects lifecycle.

Nice to have

• Expert-level experience with search platforms and deep learning training/inference platforms.
• Experience with Databricks.
• Experience with scaling and deploying Machine Learning models.

Culture & Benefits

• Flexible work options (office, home, or hybrid).
• Distributed-first company with virtual interviews and onboarding.
• Range of perks and benefits including health and wellbeing resources.
• Paid volunteer days.
• Commitment to diversity, equity, and inclusion.
• Support for accommodations or adjustments during the recruitment process.