Senior ML Platform Engineer, Training Libraries (AI)

TL;DR

Senior ML Platform Engineer, Training Libraries (AI): Building stable, scalable, and modular libraries that serve ML engineers and researchers at hirify.global, with an accent on data loading, distributed training, and checkpointing. Focus on designing and developing data libraries that allow users to create the best data sets for their training jobs and prepare the data for efficient training at scale.

Location: This is a full-time role based in our office in London. At hirify.global we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.

Company

hirify.global is the leading developer of Embodied AI technology, creating autonomy that propels the world forward with intelligent, mapless, and hardware-agnostic AI products designed for automakers.

What you will do

• Build stable, scalable, and modular libraries for data loading, distributed training, and checkpointing.
• Design and develop data libraries to create the best data sets for training jobs.
• Prepare data for efficient training at scale.
• Push up the bar for engineering excellence with a focus on maintainability and observability.
• Support ML engineers who are pushing the boundaries of scale of their models.

Requirements

• Extensive experience in programming in Python.
• A proven track record of designing, implementing, and maintaining systems at scale.
• Strong knowledge and interest in best practices for software architecture design, testing, observability.
• Experience working with concurrent, parallel, and distributed computing.
• Experience working with at least one ML framework, e.g. Pytorch, Tensorflow.
• Experience building systems in a cloud environment, ideally Azure.

Nice to have

• Experience with data pipeline orchestration tools, ideally Ray, Flyte.
• Experience with containerization and deployment frameworks, e.g. Docker, Kubernetes and Terraform.
• Experience profiling and optimising ML models e.g. with NVIDIA NSight.

Culture & Benefits

• Committed to creating a diverse, fair, and respectful culture that is inclusive of everyone.
• Value diversity, embrace new perspectives, and foster an inclusive work environment.
• Operate a hybrid working policy that combines time together in offices and workshops with time spent working from home.