Machine Learning Systems & Performance Engineer

Westbury Partners; Sydney; On-site; AUD 150,000 - 230,000; Full time

Job summary

Westbury Partners is seeking a Machine Learning Systems & Performance Engineer to build high-performance ML infrastructure spanning compilers, runtimes, optimizers and heterogeneous hardware. You will accelerate research and production workloads through deep systems-level engineering, collaborating with stakeholders to translate requirements into scalable solutions.

The role covers designing ML framework infrastructure, optimizing runtimes, and developing orchestration layers that push hardware

Qualifications

• Strong analytical and problem-solving abilities.
• Comfortable switching between multiple programming languages and technologies.
• Able to collaborate effectively with several stakeholders across the organization.

Responsibilities

• Design and develop machine learning framework infrastructure.
• Optimize runtimes and compute pipelines for hardware performance.
• Build tooling to accelerate research iteration and experimentation.
• Work with compilers, code generation, runtime systems, and orchestration layers.
• Profile hardware behavior to identify bottlenecks.
• Improve hot paths to maximize hardware utilization.
• Balance high-level architecture with low-level implementation details.
• Learn new technologies independently and evaluate approaches.

Skills

Analytical ability

Problem-solving

Collaboration

Tools

C++

Rust

Python

MLIR

CUDA

Job description

Machine Learning Systems & Performance Engineer

Westbury Partners – Sydney NSW

Full time

2d ago , from E-Financial Careers Australia

Build high-performance machine learning infrastructure spanning compilers, runtimes, optimizers, and heterogeneous hardware, accelerating research iteration and production workloads through deep systems-level engineering.

What You'll Do:

• Build optimizers, code generators, runtime libraries, and computation orchestration systems.
• Improve the speed and efficiency of machine learning research and production workloads.
• Design systems that execute computation across heterogeneous hardware.
• Work across the entire compute stack, from high-level orchestration to hardware-level performance.
• Analyze compute graphs and identify opportunities to optimize critical execution paths.
• Investigate compiler behavior and understand how software translates into hardware execution.
• Develop solutions using technologies such as C++, Rust, Python, MLIR, and CUDA.
• Collaborate with stakeholders to understand requirements and turn them into effective technical solutions.

Your responsibilities will include:

• Designing and developing machine learning framework infrastructure.
• Optimizing runtimes and compute pipelines for maximum hardware performance.
• Building tooling that accelerates research iteration and experimentation.
• Working with compilers, code generation, runtime systems, and orchestration layers.
• Profiling and analyzing hardware behavior to identify performance bottlenecks.
• Improving hot paths and extracting maximum performance from available hardware.
• Moving fluidly between high-level system architecture and low-level implementation details.
• Learning unfamiliar technologies and areas of the stack independently.
• Evaluating technical approaches and selecting the right language or tool for each problem.
• Working closely with stakeholders across the organization to identify underlying needs and deliver practical solutions.

Why Join Us:

• Work across theentire machine learning compute stack , from orchestration to hardware execution.
• Solve challenging problems involving compilers, runtimes, heterogeneous computing, and performance optimization.
• Balance rapid research iteration with production-grade performance.
• Work with modern technologies including C++, Rust, Python, MLIR, and CUDA.
• Explore problems at both architectural and extremely low-level hardware layers.
• Have the opportunity to optimize systems where every increment of performance matters.

About You:

• Comfortable moving between multiple programming languages and technologies.
• Pragmatic about technology and focused on choosing the right tool for the problem.
• Curious about how programming languages compile and execute on hardware.
• Comfortable investigating unfamiliar systems and quickly developing a working understanding.
• Interested in both high-level architecture and low-level performance engineering.
• Strong analytical and problem-solving abilities.
• Comfortable working with complex technical systems and performance-critical code.
• Effective at collaborating with stakeholders across different areas of the organization.
• Able to identify the underlying requirement behind a stakeholder's initial request.
• Excited by challenging problems where the solution is not always obvious.

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