AI / Machine Learning Engineer resume example
Experienced · US resume · set in the Engineering Compact template
Applied AI roles hire engineers who can take a model to production and prove it works. The resume should show systems you shipped, how you evaluated them, and the cost and latency trade-offs you made. Research credentials help; shipped systems decide.
A sample person: the name, employers and figures are invented. A machine learning engineer who has shipped retrieval and evaluation systems for language models, applying for an applied AI engineering role.
The order, and why
Experience first; projects and open-source; education; skills split between models and infrastructure.
Weak bullets, made strong
Weak: Worked on an LLM chatbot
Strong: Shipped a retrieval system over 400K support articles serving 30K queries a day, raising answer accuracy from 71% to 88% on a 1,200-question test set
Scale, traffic and a measured quality gain on a named evaluation.
Weak: Optimised model performance
Strong: Cut inference cost 46% by routing 70% of traffic to a smaller fine-tuned model, with no loss on the evaluation suite
A real engineering trade-off with the check that made it safe.
Mistakes to avoid
- Naming models and frameworks without saying what was built.
- Quality claims with no evaluation behind them.
- Leaving out cost and latency, which is most of the engineering.
UK CV or US resume
US: one page unless you have publications. UK and Europe: the same structure; a second page for papers is acceptable.
Live ai and ml engineering roles
Open now on the tracker, with their deadlines.
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