Systems & Research Engineer - Applied AI (Remote, Australia)

About the Role

Join a rapidly growing US-based applied AI firm that creates production AI systems for the freight and global supply-chain sector. Founded by engineering experts from MIT and Stanford with $4.5M seed funding, the company operates high-impact AI products such as a fraud and identity screening platform used daily to vet approximately 5,000 drivers. Work fully remotely from anywhere in Australia or across the globe, with no relocation or mandatory office attendance, and work asynchronously without aligning to US time zones.

Key Responsibilities

• Explore the operational behavior of deployed AI systems, identifying performance bottlenecks related to GPU, CPU, memory, network, or serving architecture.
• Create benchmarks to test different architectures, models, and inference approaches, using experimental results to drive engineering decisions.
• Design and execute experiments evaluating speech-to-text and other ML model serving strategies, balancing quality, cost, and concurrency.
• Engage deeply with distributed systems involving models in the loop to optimize inference throughput and latency.
• Develop infrastructure to evaluate model performance using real production traffic and convert research insights into production-level solutions.

Qualifications and Requirements

• Experience in research or engineering roles focused on machine learning systems, AI infrastructure, inference engineering, or performance optimization.
• Familiarity with benchmarking, profiling, and evaluating large-scale ML infrastructure, model serving, and inference pipelines.
• Proven ability to apply the scientific method rigorously to AI systems: form hypotheses, run controlled experiments, analyze data, and effect impactful changes based on findings.
• Competence in working with coding agents and modern software development tools to enhance productivity and engineering quality.
• Exceptional written communication skills suitable for asynchronous global collaboration and documentation.

Preferred Experience

• Work experience comparable to roles within Google, Meta, Stripe, Amazon AGI, or notable AI startups and research labs focusing on ML infrastructure.
• Engineering background with strong understanding of production AI systems having a significant model component integrated within distributed systems.

What This Role Is Not

• This is not a DevOps, frontend, conventional full-stack, or Web3 infrastructure position.
• Pure distributed systems experience without AI/model integration is insufficient.

Work Philosophy

• The team operates asynchronously with an emphasis on quality engineering, meaningful ownership, and clear communication.
• Coding agents are heavily leveraged to focus engineers on decision-making rather than manual code writing.

Compensation

Competitive salary up to approximately A$420,000 plus equity participation. This reflects a conversion from a US dollar range of approximately US$220,000 to US$300,000, intended to offer elite-level US AI engineering compensation accessible from Australia.

Additional Notes

• The hiring bar is extremely high; candidates must demonstrate outstanding contributions in AI systems and research engineering, framing their achievements through detailed experimental design and data-driven outcomes.