Machine Learning Engineer CV example

What recruiters expect

Before writing anything, it helps to know what the person reading is checking for. In this field that is usually a short, specific list:

• Serving experience with numbers: p95 latency, requests per second, batch sizes.
• A retraining pipeline that runs without manual steps, and what triggers it.
• Strong software engineering: testing, code review, packaging, CI. ML engineers are engineers first.
• Cost awareness, particularly GPU spend and where inference can be moved to cheaper hardware.
• Model governance: versioning, reproducibility, rollback, and what happens when a model must be withdrawn.

Recommended CV structure

This is the running order the example uses. It is a starting point rather than a rule, but the order reflects what tends to be read first in this profession.

1. Profile — Three or four lines positioning you for the role.
2. Technical Skills — Grouped skills, for example "Languages" and "Tooling".
3. Professional Experience — Paid roles, in reverse chronological order.
4. Platform Projects — Work you built, with outcomes and the stack used.
5. Education — Degrees, diplomas and school-leaving qualifications.
6. Certifications — Completed certifications with the issuing body.
7. Languages — Spoken languages with CEFR levels.

Sections worth adding

• Publications — Include if you have applied papers or an accepted workshop submission.
• Open Source — Contributions to serving or pipeline tooling are particularly relevant here.

Skills worth including

Grouped rather than listed in one block. Grouping makes a long list readable and shows that you can tell the difference between the things you use daily and the things you have touched.

Beyond the technical list: Working between data science and platform teams, Design documents, Cost conversations, On-call for model services. These belong inside your experience bullets, demonstrated, rather than in a list of adjectives.

Example professional summary

Three or four lines, positioned for the role rather than describing your personality. Two versions you can adapt:

Machine learning engineer with five years building and operating model-serving infrastructure. Recent work covers a Triton cluster holding p95 inference at 45ms across four models, a drift-triggered retraining pipeline, and a 58% reduction in monthly inference cost through quantisation.

ML engineer with a backend engineering background. Comfortable owning the path from a data scientist’s notebook to a monitored, versioned, rollback-capable service - and the on-call rota that comes with it.

Writing your experience

The difference between a CV that gets a call and one that does not is almost always in the bullet points. Each pair below shows a real rewrite of the kind of line that appears on most CVs in this field.

Taken from the example

The sample CV for this profession is fully written. A few sections from it, so you can see the level of specificity that works:

Experience

Machine Learning Engineer, Fenland Diagnostics Technology

• Moved four models from notebook hand-off to a Triton serving cluster on Kubernetes, holding p95 inference at 45ms for around 700 requests a second with autoscaling on GPU utilisation.
• Built an Airflow retraining pipeline triggered by a drift threshold rather than a fixed schedule, with automatic promotion only when the challenger beat the champion on a held-out week of live traffic.
• Cut monthly inference cost by 58% by quantising two models to INT8 with ONNX Runtime and moving the smaller one from GPU to CPU instances with no measurable accuracy loss.
• Introduced model versioning and one-command rollback after a bad promotion required a four-hour manual recovery; subsequent rollbacks have taken under two minutes.

Projects

• Inference cost dashboard — Internal tool attributing GPU cost per model and per customer, which made the quantisation case measurable and led directly to the 58% reduction.

Education

• MEng Computer Science, University of Manchester — First Class Honours

Certifications and registration

• Certified Kubernetes Application Developer (CKAD) — CNCF

Common mistakes

ATS considerations

Applicant tracking systems behave differently by sector, and generic advice is often wrong for a given field. These points are specific to machine learning engineer applications:

• Include "MLOps" and "model deployment" as phrases; they are the two most common filters for this title.
• Name the serving stack explicitly - Triton, TorchServe, SageMaker endpoints - rather than only "deployed models".
• Keep "software engineering" vocabulary on the CV: testing, code review, CI. Many ML engineer adverts are written by platform teams.
• State GPU experience precisely, including the hardware class where it is relevant.

The Minimal ATS layout is built for this, and the ATS guide covers what parsers do to a file in more detail.

Guides that go deeper

This page applies general CV advice to one field. The full treatment of each part is here:

• How to Write a Software Developer CV — What technical reviewers look for, why the stack list is not enough, and how to write about work you cannot show.
• How to Write a Data Analyst CV — Every bullet point should end in a decision someone made differently. Dashboards with no audience are activity, not impact.
• How to List Skills on a CV — Group them, be specific, and cut anything you would not want to be interviewed on for ten minutes.
• How to Describe Work Experience — The difference between listing duties and demonstrating impact, and a repeatable way to rewrite any weak bullet point.

Questions about machine learning engineer CVs