Match Jobs for Machine Learning Researcher Roles
A quick example on how you can match against listings with Machine Learning Researcher profiles through our job feed data API with semantic search capabilities.
This basic example demonstrates how to use the vector embeddings and vector search capabilities of the jobdata API to match Machine Learning Researcher job profiles based on semantic similarity. This way you can find job listings that align closely with a given job profile description, even if the exact keywords are not present in the job description. This is particularly useful for HR platforms, recruitment tools, and job boards aiming to improve candidate-job matching accuracy.
By leveraging our pre-generated embeddings and semantic search features, you can unlock powerful semantic matching capabilities to enhance your job search or recruitment platform. Whether you're matching candidates to jobs or analyzing job market trends, these features provide a robust solution for understanding and matching job profiles based on context and meaning.
Job Matching Example
The API provides pre-generated vector embeddings for all job posts, currently created using OpenAI's `text-embedding-3-small` model. These embeddings capture the semantic meaning of job titles and descriptions, enabling the API to perform semantic searches based on a job profile description.
When you provide a job profile description (e.g., "Focused on advancing ML algorithms, publishing research, and applying cutting-edge techniques to solve complex problems."), the API:
- Converts the text into a vector embedding in real-time.
- Compares this embedding against the pre-generated embeddings of job posts using cosine similarity.
- Returns job listings that are most semantically similar to the profile, ordered by relevance.
Here's a link to a live API results page:
Focused on advancing ML algorithms, publishing research, and applying cutting-edge techniques to solve complex problems.
The results include a cosine_dist field, which indicates the similarity between the query and each job post. Lower values represent higher semantic similarity.
Query the jobdata API
Example search request (with header auth):
curl -X GET -H \
"Authorization: Api-Key YOUR_API_KEY" \
https://jobdataapi.com/api/jobs/?max_age=90&vec_text=Focused on advancing ML algorithms, publishing research, and applying cutting-edge techniques to solve complex problems.
Example search request (with auth param):
curl https://jobdataapi.com/api/jobs/?api_key=<YOUR API KEY>&max_age=90&vec_text=Focused on advancing ML algorithms, publishing research, and applying cutting-edge techniques to solve complex problems.
Shown below is a live example of what a list with the top matched jobs could look like, filtered by the job position profile from above with the most recent data from our global database:
Staff Research Engineer, Applied ML
London, UK
Full TimeSenior level
Campus ML Engineer (Intern)
London
InternshipEntry-level
Machine Learning Researcher-Search
Singapore, Singapore
Full TimeSenior level
Senior ML Research Engineer
Milpitas, CA
Full TimeSenior level
Staff Machine Learning Engineer - Applied ML & Research
Croatia
Full TimeSenior level
Campus ML Engineer (Full-Time)
London
Full Time
Staff Research Scientist, Machine Learning Efficiency
Sydney NSW, Australia
Full TimeSenior level
Machine Learning Researcher
Sydney, Australia
Full TimeMid-level
AI/ML Engineer
Infopark, Kochi, India
Full TimeMid-level
Researcher - Large Language Models, Applied Machine Learning
San Jose, California, United States
Full TimeExecutive level
Find out more on how to use the jobdata API in our docs.