Systems & Research Engineer | Applied AI | Up to A$420K + Equity | 100% Remote from Australia | US AI Company | Fully Async

Location: 100 per cent remote from Australia. Sydney, Melbourne, Brisbane, Perth, Byron Bay or pretty much anywhere you like. No relocation, no office mandate and no requirement to work US hours. Type: Full-time Salary: Up to approximately A$420K + equity "I've seen things you people wouldn't believe." Blade Runner Now we would like to see what you have built. Stay in Australia, work with a US AI company Imagine living in Sydney, Melbourne, Brisbane, Perth or Byron Bay while working remotely with an exceptional US applied AI company. This company operates globally and asynchronously. You are judged on the quality of your engineering, research and decisions, not on whether your green Slack light is on at 3am. We are recruiting a Systems and Research Engineer for a fast-growing US applied AI company building production systems for the freight and global supply chain industry. Founded by engineers from MIT and Stanford, the business raised US$4.5M in seed funding in January 2026 and is already operating AI products at meaningful real-world scale. One of its core fraud and identity platforms now screens approximately 5,000 drivers every day. The team is small. The ambition is not. And the engineering bar is deliberately very high. What you will actually do This is not another generic AI Engineer position. You will sit at the intersection of AI systems, research, inference, model serving, performance engineering, distributed systems and evaluation. You will investigate how production AI systems actually behave. Where is the bottleneck? GPU, CPU, memory, network, serving architecture, concurrency or model choice? You will form hypotheses, build benchmarks, test alternatives and use the results to make real engineering decisions. A recent example involved benchmarking different speech to text approaches and developing a hybrid open source and production system that outperformed vendor alternatives across both quality and economics. That is the flavour of problem we are talking about. We want the experiment, not just the percentage A resume saying "reduced inference latency by 37 per cent" is not enough. We want to know:

• What was the baseline?
• What did you think was happening?
• How did you test it?
• What alternatives did you benchmark?
• How did you control for bias in the experiment?
• What did the data reveal?
  
  

And crucially, what engineering decision changed because of your findings? You need to be able to walk us through at least one serious benchmark or experiment you personally designed and ran involving areas such as model serving, inference, evaluation, retrieval, speech systems or agent infrastructure. The team wants engineers capable of applying the scientific method to production AI systems. The kind of work you could be doing

• Profiling AI systems and identifying GPU, CPU, memory or network bottlenecks
• Benchmarking serving frameworks such as vLLM, SGLang and alternative architectures
• Investigating inference throughput and latency
• Evaluating open source versus closed source models
• Optimising workloads across cost, quality and concurrency
• Building evaluation infrastructure
• Understanding model behaviour under real production traffic
• Reasoning about distributed systems where there is genuinely a model in the loop
• Turning research findings into production architecture
• Proving, occasionally, that everybody's first assumption was wrong
  
  

Who Could Be Right Your Current Title Might Be

• Research Engineer
• ML Systems Engineer
• AI Infrastructure Engineer
• Inference Engineer
• Performance Engineer
• ML Platform Engineer
• Systems Engineer
  
  

Experience inside sophisticated ML infrastructure environments is highly relevant. Think engineering problems similar to those encountered at Google, Meta, Stripe, Amazon AGI or exceptional AI startups and research organisations. But a famous company on your CV is not enough. The founders want to see exceptional work. The strongest candidates can explain their experience like this: here was the hypothesis, here was the baseline, here was the benchmark, here is what we discovered, here is what we changed. What this role is not This is not DevOps. It is not frontend. It is not conventional full stack development. It is not Web3 infrastructure. And pure distributed systems experience, however impressive, is not enough if there has been no meaningful AI or model component. There needs to be a model in the loop. AI coding agents This engineering team uses coding agents extensively, every day. The philosophy is simple. Great engineers should increasingly spend their time deciding what should be built, how it should work and whether the result is correct, rather than manually typing every line. Your ability to work effectively with modern coding agents will form part of the interview process. Work from Australia, or anywhere This is genuinely remote. Sydney. Melbourne. Brisbane. Perth. London. Singapore. Berlin. Toronto. San Francisco. It does not really matter. There is a San Francisco office available if you happen to want it. You absolutely do not need to use it. The team works asynchronously across global time zones, with strong written communication and genuine ownership expected. Compensation Up to approximately A$420K + equity. The underlying compensation range is approximately US$220K to US$300K, with US$300K as the hard ceiling. The Australian dollar figure is an approximate currency conversion and will naturally move with exchange rates. This is an opportunity to stay in Australia while accessing compensation normally associated with elite US AI engineering roles. The bar One of the founders has a very simple hiring philosophy. He wants engineers joining the company who are better than the engineers already there. That makes the bar high, deliberately. But if you have done genuinely exceptional work around AI systems, inference, model serving, evaluation or performance engineering, we want to hear from you. And when you apply, do not just tell us what you built. Tell us about the experiment.