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

> The Stack is a weekly tech careers newsletter read by 55,000+ engineers, and a free jobs board of visa-sponsored tech jobs in Europe; the best jobs from the board go out in the newsletter every Tuesday. This is one job from the board.

> Responsibilities: - Define and own the end-to-end ML platfor

- **Company:** Neara (https://jobs.trythestack.co/companies/neara)
- **Location:** London, United Kingdom
- **Work arrangement:** Hybrid
- **Employment type:** Full-time
- **Experience level:** 10+ years
- **Visa sponsorship:** Offered, according to the listing
- **Key skills:** ML Platform Strategy, Distributed Systems, Model Serving, Training Pipelines, Python, PyTorch, Kubernetes, Docker, AWS, GCP, Azure, CUDA, System Design, MLOps, Data Warehousing, Model Monitoring
- **Posted by employer:** 2026-07-14
- **Added to The Stack:** 2026-07-14
- **Status:** May no longer be active (posted more than 60 days ago)
- **Apply:** https://jobs.ashbyhq.com/neara/f4a27471-b8c0-4331-ae01-aba5caab1703 (the employer's own application page)
- **Human page:** https://jobs.trythestack.co/jobs/staff-machine-learning-engineer-platform-australia-based-full-relocation-provided-neara

## 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.

## FAQ

- **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.

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