neara - Staff Machine Learning Engineer (Platform), Australia-based, Full Relocation Provided

London+ Equity1mo ago

In Office Staff EMEA Cloud Computing Artificial Intelligence Machine Learning Engineer Staff Engineer Python MLOps Data Quality Documentation GCP AWS Azure Kubernetes Docker CUDA

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

Responsibilities

• 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.

Benefits

• Full relocation to Australia • 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 • To apply, please use the online application link below. Neara values diversity, belonging and equal employment opportunities. We encourage individuals from all backgrounds to apply. • No agencies or third-party service providers, please.