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

Systems & Research Engineer | Applied AI | Up to A$420K + Equity

100% Remote from Australia | US AI Company | Fully Async

Model Serving | Inference | ML Systems | Performance Engineering

“I’ve seen things you people wouldn’t believe.”

— Blade Runner

Now we’d like to see what you’ve built.

Imagine living in Sydney, Melbourne, Brisbane, Perth, Byron Bay — or pretty much anywhere you like — while working remotely with an exceptional US applied AI company.

No relocation.

No five-day office mandate.

And importantly, no requirement to work US hours.

This company operates globally and asynchronously. You’re judged on the quality of your engineering, research and decisions, not whether your green Slack light is on at 3am.

We’re recruiting a Systems & 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 isn’t.

And the engineering bar is deliberately very high.

What You’ll Actually Do

This is not another generic “AI Engineer” position.

You’ll sit at the intersection of:

AI systems.

Research.

Inference.

Model serving.

Performance engineering.

Distributed systems.

Evaluation.

You’ll investigate how production AI systems actually behave.

Where is the bottleneck?

GPU?

CPU?

Memory?

Network?

Serving architecture?

Concurrency?

Model choice?

You’ll 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/production system that outperformed vendor alternatives across both quality and economics.

That’s the flavour of problem we’re talking about.

We Want the Experiment, Not Just the Pe

rcentage

A resume saying:

“Reduced inference latency by 37%.”

isn’t 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

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.

And 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’s what we discovered.

Here’s what we changed.

What This Role Is NOT

This is not DevOps.

It isn’t frontend.

It isn’t conventional full-stack development.

It isn’t 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 doesn’t 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–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’ve done genuinely exceptional work around AI systems, inference, model serving, evaluation or performance engineering, we want to hear from you.

And when you apply, don’t just tell us what you built.

Tell us about the experiment.