Where AI & ML Engineer resumes lose out.
And how to fix each gap.
About half already cite papers or patents, yet the fundamentals rarely appear. We have spent years hiring for AI, data and analytics roles, and these are the gaps we see most in AI & ML Engineer resumes. None of them is about ability, and every one is fixable.
Where AI & ML Engineer resumes lose out
AI and computer vision engineers have close to the opposite problem to data engineers. Many already have public proof, but the resume does not turn it into something a recruiter can act on.
A paper does not say what you built
About half
of resumes cite a paper, a publication or a conference, and a few mention a patent. The proof exists.
Publication language and resume language are different languages. A paper title tells a recruiter that you did research. It does not say what you built, what it ran on, or who used it. What is missing is not evidence but translation.
Co-authored a paper on defect detection using deep learning.
Turned our defect detection research into a line-side inspection model that flags scratches within 40 ms per frame, running on edge GPUs at two plants.
An illustrative example.
Translate each paper into four lines
- The problem, in the customer's words rather than the paper's.
- What you built or decided, and your part in it.
- Where it ran: the hardware, the latency budget, the data volume.
- What changed because of it: a metric, a decision, a user.
The fundamentals never appear
Almost none
of resumes use any of the vocabulary a technical screen relies on: anchor boxes, IoU thresholds, NMS, backpropagation, positional embeddings or cosine similarity.
This is not a resume-writing failure. A resume says what you built. A technical screen tests whether you understand why it worked: which hyperparameters you tuned and what they did, how you stopped overfitting, how a vision transformer differs from a CNN. Better bullets cannot close that gap, because a resume was never the right place for it.
Trained object detection models using YOLO and PyTorch.
Retuned anchor sizes and the IoU threshold after the defaults missed small scratches, then raised the NMS threshold so neighbouring defects stopped being merged into one.
An illustrative example.
Explain it, do not just list it
- Which hyperparameter did you change, and what happened to the result?
- How did you know the model was overfitting, and what fixed it?
- What would break if the training data changed?
- Why this architecture and not the obvious alternative?
- Say it in your own words, as if to a colleague. That is the version an interviewer is listening for.
Tracking and deployment are thin
3 in 4
describe real-time work, but almost none name an experiment tracker such as MLflow, and only about 4 in 10 name ONNX or TensorRT.
Real-time is a claim about latency, hardware and cost, and the resume rarely says which. How a model reached production, and how you knew it kept working, is what separates a research prototype from a shipped system.
Deployed real-time models to production.
Exported to ONNX and quantised to INT8 to fit a 30 ms budget on an edge GPU, and tracked every training run in MLflow so a regression could be traced back to a data change.
An illustrative example.
Tell the deployment story
- The latency or throughput budget you had to meet, and the hardware.
- What you did to get there: export format, quantisation, batching, profiling.
- How you knew it kept working: monitoring, drift checks, a retraining trigger.
- How runs were tracked, so a result could be reproduced.
Your title is not your specialisation
2 in 3
have a job title that no other ML or AI resume in our set shares.
Machine learning engineer, AI engineer and computer vision engineer are used almost interchangeably, sometimes within a single resume. A recruiter searching one label misses people who use another.
Machine Learning Engineer | AI Engineer | Computer Vision
Computer vision engineer: object detection and defect inspection on edge GPUs.
An illustrative example.
Make it findable
- State your specialisation in the first line, in the words a recruiter would search.
- Name the domain you work in, such as manufacturing inspection or medical imaging.
- Name one thing you shipped in that domain.
Also worth fixing
About 4 in 10
say they led something
Almost none mention code review. Senior roles usually involve leading design and reviewing other people's work, so if you do, say so and give one example.
1 in 5
of ML and AI resumes use the word “owned”
That is the highest of any role we looked at, and it is still a minority. If you own outcomes, make it the first thing a recruiter reads, not the fifth.
Figures come from two small sets, 21 AI and computer vision resumes and 15 ML and AI Engineer resumes from candidates who had reached an interview stage, all from a few job postings, so they are shown as loose proportions. The before and after examples are illustrative, not taken from any candidate.
Keep going
- How to answer “Tell me about a production incident”
- How to put a real number on your data work
- How to show ownership, not just contribution
- How to answer “Walk me through a pipeline you built”
- How to answer “Tell me about a model in production”
- Check one of your resume bullets for free
- Where Data Engineer resumes lose out
- Where Data Scientist resumes lose out
Make your work impossible to overlook.
Start with your resume. graph shows you what it understood and what is missing.