Big Wave Digital
Systems & Research Engineer | Applied AI | Up to £220K + Equity 100% Remote from the UK | US AI Company | Fully Async
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Job Description: Systems & Research Engineer
“AI has the potential to be one of the most important and beneficial technologies ever invented.”
— Demis Hassabis, Co-founder & CEO, Google DeepMind
Perhaps you already work at a foundational AI lab.
Perhaps you’ve spent the last few years deep inside model serving, inference infrastructure, or GPU performance at Google DeepMind, Anthropic, Meta, Amazon, NVIDIA, Together AI, Fireworks AI, Baseten, Anyscale, or Modal.
Perhaps most people outside your team don’t entirely understand what you do.
You know who you are.
We’d like to talk.
We’re recruiting a Systems & Research Engineer for a fast-growing US applied AI company building production AI systems for freight and global supply chains.
Founded by engineers from MIT and Stanford, the company raised US$4.5M in seed funding in early 2026 and is already operating 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 problems aren’t.
And the engineering bar is exceptionally high.
What You’ll Actually Do
This is not another generic AI Engineer position.
You’ll work at the intersection of:
- AI Systems
- Model Serving
- Inference
- Performance Engineering
- Research
- Distributed Systems
- Evaluation
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Your job is to understand what is really happening inside production AI systems.
Where is the bottleneck?
- GPU?
- CPU?
- Memory?
- Network?
- Concurrency?
- Serving architecture?
- Model choice?
You’ll form a hypothesis, establish a baseline, design the benchmark, run the experiment, and use the results to make an engineering decision.
A recent example involved benchmarking speech-to-text providers and building a hybrid open-source/production architecture that beat vendor alternatives on both cost and quality.
That is the flavor of problem we’re talking about.
We Want the Experiment, Not Just the Percentage
Your CV says:
“Reduced inference latency by 37%.”
Excellent.
Now tell us:
- What was the baseline?
- What did you believe was causing it?
- What alternatives did you test?
- How did you design the benchmark?
- What could have biased the results?
- What did the data reveal?
- And most importantly:
What decision changed because of your experiment?
This is fundamental to the role.
You should be able to walk us through a serious benchmark or experiment you personally designed and ran involving model serving, inference, evaluation, retrieval, speech, or agent infrastructure.
The strongest candidates think like scientists and build like engineers.
What You Could Be Working On
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- Profiling production AI systems for GPU, CPU, memory, and network bottlenecks.
- Benchmarking vLLM, SGLang, and alternative serving architectures.
- Investigating inference throughput and latency.
- Optimizing workloads across cost, quality, and concurrency.
- Evaluating open-source versus proprietary models.
- Building evaluation infrastructure.
- Understanding model behavior under real production traffic.
- Making distributed-systems decisions where there is actually a model in the loop.
- And occasionally discovering that the obvious answer was completely wrong.
Who Are We Looking For?
Your current title might be:
- Research Engineer
- ML Systems Engineer
- AI Infrastructure Engineer
- Inference Engineer
- Performance Engineer
- ML Platform Engineer
- Systems Engineer
Maybe you’re sitting somewhere inside DeepMind thinking about your next move.
Maybe Anthropic.
Maybe NVIDIA.
Maybe an inference company almost nobody outside AI infrastructure has heard of yet.
Perfect.
Company pedigree helps, but it won’t get you the job.
Exceptional work will.
We want engineers.
Systems & Research Engineer | Applied AI | Up to £220K + Equity
100% Remote from the UK | US AI Company | Fully Async
Model Serving | Inference | ML Systems | Performance Engineering
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