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

Neara

Open Positions (1)

Engineering

Staff Machine Learning Engineer – Platform