Identify if resume is for ml engineering
using AI
Below is a free classifier to identify if resume is for ml engineering. Just input your text, and our AI will predict if the resume is suitable for a machine learning engineering position - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("if-resume-is-for-ml-engineering", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/if-resume-is-for-ml-engineering/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/if-resume-is-for-ml-engineering/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict if the resume is suitable for a machine learning engineering position.
This pretrained text model uses a Nyckel-created dataset and has 2 labels, including Ml Focused and Not Ml.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if the resume is suitable for a machine learning engineering position).
Whether you're just curious or building if resume is for ml engineering detection into your application, we hope our classifier proves helpful.
Related Classifiers
Need to identify if resume is for ml engineering at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Automated Resume Screening: This function can streamline the hiring process by automatically filtering out resumes that do not fit within the machine learning engineering category. Recruiters can focus their efforts on qualified candidates, reducing the time spent on initial resume reviews.
- Talent Pool Management: Companies can use this classifier to build and maintain a database of qualified candidates for future open positions. By tagging resumes as relevant or not, organizations can easily access a curated talent pool when new roles arise.
- Job Market Insights: HR departments can analyze the number of resumes classified as relevant for machine learning engineering roles over time. This data can provide valuable insights into trends in job demand and help guide recruitment strategies accordingly.
- Personalized Candidate Outreach: Recruiters can use classification results to tailor outreach efforts to candidates based on their resume's relevance. This personalization can enhance engagement by targeting communication and job opportunities more effectively.
- Resume Feedback and Improvement: The classification function can be used to provide feedback to job seekers on the relevance of their resumes for machine learning roles. This can help candidates understand how to tailor their applications, improving their chances of landing a job.
- Diversity Hiring Initiatives: Organizations can ensure compliance with diversity hiring initiatives by using the function to track the diversity of applicants for ML engineering positions. This data can be pivotal in evaluating and improving diversity in hiring practices.
- Continuous Learning and Adaptation: The classifier can evolve with changing job requirements in the machine learning field. By regularly updating the model with new data, companies can ensure they are evaluating resumes against the most current industry standards and expectations.