Staff Machine Learning Engineer – Platform

Role Overview

• Own the ML platform strategy end-to-end
• Define and drive the multi-year technical roadmap for training pipelines, serving architecture, experiment management, and monitoring systems that tie it all together.
• Build tooling that accelerates ML delivery
• Develop foundational infrastructure that takes engineers from idea to production faster, standardising workflows and eliminating friction between experimentation and deployment.
• Solve hard distributed systems problems
• Enable training across distributed data with residency and security requirements, while ensuring models run efficiently across varied GPU hardware, including sparse tensor implementations and architecture bottlenecks.
• Design scalable, flexible serving architecture
• Define serving systems that handle spiky load in production while giving the ML team the freedom to experiment across regions, customers, tasks, and verticals.
• Unblock the ML team at scale
• Identify what's slowing the team down, define the contracts and interfaces between training, evaluation, and serving, and build the roadmap to turn ambitious research into routine delivery.

Requirements

• A foundation in R&D to help drive the right direction and prioritisation necessary for faster iteration.
• Demonstrated ability to set ML platform standards and interactions across teams, influence engineering roadmaps without direct authority, and drive alignment on complex infrastructure decisions.
• Significant technical experience running deep learning at scale, with a track record of designing and operating the systems other ML engineers depend on.
• Experience in building training data warehouses as well as bringing data systems to ML readiness.
• Deep hands-on expertise building ML infrastructure at scale, in particular: training pipelines, distributed compute, model serving and model monitoring.
• Deep familiarity with model monitoring, data quality frameworks, and the operational practices required to maintain a diverse portfolio of production ML models.
• A proven investment in building others through documentation, internal standards, and raising the MLOps capability of the engineering discipline around you.
• Strong proficiency in Python, PyTorch (or equivalent framework) and a passion for deep learning.
• Demonstrated software engineering fundamentals across system design, code quality, and scalability, with a clear instinct for where to invest complexity and where to keep things simple.
• Solid experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker) as well as dealing with custom on-prem/neocloud offerings
• Proficiency in writing and optimising custom CUDA kernels for deep learning training is a nice-to-have but not imperative

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Kubernetes
• Python
• PyTorch

Benefits

• Full relocation to Australia
• Competitive salary
• Meaningful ESOP
• Fully flexible work environment. We have a fully stocked office (and an impressive snack collection) in Redfern.
• Regular office events
• The real benefit is working on a genuinely complex, innovative and industry-leading product, making a genuine difference in the world around us