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About the Role
Neara is creating reality by using advanced machine learning to build digital twins of electricity grids. We simulate extreme weather and stress to help utilities optimize investments and build a more resilient global energy future. This role is critical in enabling the rapid development and efficient deployment of frontier-level spatial intelligence models.
Responsibilities
- Own the ML platform strategy end-to-end, defining and driving the technical roadmap for training pipelines, serving architecture, experiment management, and monitoring systems.
- Build tooling that accelerates ML delivery by developing foundational infrastructure to streamline the process from idea to production.
- Solve complex distributed systems problems to enable distributed data training with residency and security requirements, ensuring efficient model execution across varied GPU hardware.
- Design scalable, flexible serving architecture to handle spiky production loads while allowing the ML team freedom to experiment.
- Unblock the ML team at scale by identifying bottlenecks, defining interfaces between training, evaluation, and serving, and building a roadmap for routine delivery of ambitious research.
Requirements
- A foundation in R&D to drive direction and prioritization for faster iteration.
- Demonstrated ability to set ML platform standards, influence engineering roadmaps, and drive alignment on complex infrastructure decisions.
- Significant technical experience running deep learning at scale, with a track record of designing and operating systems for other ML engineers.
- Experience in building training data warehouses and bringing data systems to ML readiness.
- Deep hands-on expertise building ML infrastructure at scale, including training pipelines, distributed compute, model serving, and model monitoring.
- Deep familiarity with model monitoring, data quality frameworks, and operational practices for maintaining production ML models.
- Proven investment in building others through documentation, internal standards, and raising MLOps capability.
- 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.
- Solid experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
- Experience dealing with custom on-prem/neocloud offerings.
- Proficiency in writing and optimizing custom CUDA kernels for deep learning training is a nice-to-have.
Skills
- Machine Learning
- Deep Learning
- Python
- PyTorch
- Distributed Systems
- Cloud Infrastructure (AWS, GCP, Azure)
- Kubernetes
- Docker
- MLOps
- Data Warehousing
- Model Serving
- Model Monitoring
- CUDA Kernels
Location
- Sydney, Australia
Work Type
- Full-time
- Flexible Work Environment
Experience Level
- Staff
Salary/Compensations
- Competitive salary
- Relocation package
- Visa sponsorship
Benefits
- Full relocation to Australia
- Meaningful ESOP
- Fully Flexible Work Environment
- Fully stocked office
- Impressive snack collection
- Regular office events
About the Company
- Neara uses advanced machine learning to create engineering-grade, physics-enabled digital twins of electricity grids across four continents.
- We simulate extreme weather and structural stress at a network-wide scale, empowering global utilities to pinpoint risks, optimize investments, and build a more resilient energy future.
- Our team consists of brilliant minds dedicated to making a tangible difference in the real world, utilizing AI and machine learning.
- We foster a culture where innovation thrives, with everyone owning the mission.
Equal Opportunity
- Neara values diversity, belonging and equal employment opportunities. We encourage individuals from all backgrounds to apply.