Staff Machine Learning Engineer (Platform) - Australia based - Full Relocation Provided at Neara

✨ The Stack take on this Neara job

• 🏢 This is Neara's only job here right now
• 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
• 💡 No salary shown. Ask Neara if it is above the minimum.

Updated 2026-11-05, from our data and official sources.

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About the role

Responsibilities:
- Define and own the end-to-end ML platform roadmap: training pipelines, model serving, experiment management and monitoring.
- Build developer-facing tooling to shorten cycle time from prototype to production and standardise workflows.
- Design and operate scalable distributed training and serving systems that respect data residency and security constraints.
- Optimise performance across varied GPU hardware (including CUDA and sparse tensor implementations) and orchestrate multi-node training/serving on Kubernetes.
- Ensure reliable deployment and monitoring for frontier spatial models used by global utility customers; collaborate with research, product and infra teams.
- Mentor engineers on MLOps best practices and elevate platform reliability and cost-efficiency.

Requirements:
- 10+ years engineering experience with demonstrable ownership of platform or infrastructure systems.
- Strong Python and PyTorch experience; solid exposure to CUDA and performance optimisation for training/serving.
- Proven experience with Kubernetes, Docker, cloud providers (AWS/GCP/Azure) and containerised deployments.
- Deep understanding of distributed systems, system design, model serving, training pipelines, experiment tracking and observability.
- Experience with geospatial/point-cloud workflows, data unification or similar uncommon data types is highly desirable.
- Excellent cross-team communication and experience delivering production ML to customers; willingness to relocate to Sydney with provided sponsorship.

Responsibilities

Own the end-to-end ML platform strategy, including the technical roadmap for training pipelines, serving architecture, and monitoring systems. Build foundational infrastructure to accelerate the transition of ML research into production-ready delivery for global utility customers.

Requirements

Requires significant technical experience running deep learning at scale and designing infrastructure for other ML engineers. Proficiency in Python, PyTorch, cloud platforms, and container orchestration is essential, with a strong foundation in software engineering and distributed systems.

Key skills

• ML Platform Strategy — not in any other listing on The Stack
• Distributed Systems — in 342 other listings on The Stack
• Model Serving — in 9 other listings on The Stack
• Training Pipelines — in 2 other listings on The Stack
• Python — in 1200 other listings on The Stack
• PyTorch — in 92 other listings on The Stack
• Kubernetes — in 585 other listings on The Stack
• Docker — in 464 other listings on The Stack
• AWS
• GCP
• Azure
• CUDA
• System Design
• MLOps
• Data Warehousing
• Model Monitoring

On The Stack (as of 2026-11-05)

• This is Neara's only listing on The Stack.
• 214 visa-sponsored jobs in the United Kingdom on The Stack, posted in the last 60 days.
• 29 visa-sponsored machine learning engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Neara

Is this Staff Machine Learning Engineer (Platform) - Australia based - Full Relocation Provided role still open?

It was posted on 2026-07-14. Listings more than 30 days old may no longer be active, so check Neara's site before applying.

Does Neara offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Neara's site.

Where is this role located?

The listing gives the location as London, United Kingdom. It is marked hybrid.

What skills does this role ask for?

The listing names: ML Platform Strategy, Distributed Systems, Model Serving, Training Pipelines, Python, PyTorch, Kubernetes, Docker, AWS, GCP, Azure, CUDA, System Design, MLOps, Data Warehousing, Model Monitoring.