Medeloop

AI-driven platform for clinical research management

Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeSydney NSW, AustraliaIn-person

About the Job

Requirements

A minimum of two years of full-time industry experience using machine learning, or PhD-level research that resulted in building a recommendation system for a particular domain.
A postgraduate degree in computer science, statistics, or a related subject.
Solid and demonstrable knowledge of machine learning principles.
Solid and demonstrable knowledge of recommender systems and relevant approaches such as collaborative filtering, matrix factorization techniques, and multi-armed bandits.
Experience building and deploying practical recommender systems leveraging big data sources using technologies such as Apache Spark and Neo4J.
Excellent programming skills in a language such as Java or Python.
Experience managing Hadoop, Spark, or Mesos clusters.
Experience in data analysis using software such as R or MATLAB.

Responsibilities

Parse and extract key information from uploaded resumes to automatically populate candidate profiles.
Perform identity resolution methods to deduplicate profiles coming from multiple sources.
Analyze candidate profiles to predict whether they are likely to be suitable for a particular job description.
Analyze job descriptions to predict whether they are likely to be effective in attracting candidates.
Build predictive models for candidate sourcing methods.

Machine Learning Engineer

Medeloop · Sydney, Australia · Full-time · Posted 2025-11-16

Salary: USD 30,000–60,000

Workplace: on_site

Department: Engineering

Description

We are looking for a Machine Learning Engineer.

Responsibilities:

Parsing and extracting key information from uploaded resumes in order to automatically populate candidate profiles
Performing identity resolution methods to deduplicate profiles coming from multiple sources
Analyzing candidate profiles in order to predict whether they are likely to be suitable for a particular job description
Analyzing job descriptions to predict whether they are likely to be effective in attracting candidates
Building predictive models for candidate sourcing methods

Requirements

A minimum of two years of full time industry experience using machine learning or PhD level research that resulted in building a recommendation system for a particular domain
A postgraduate degree in computer science, statistics or a related subject
Solid and demonstrable knowledge of machine learning principles
Solid and demonstrable knowledge of recommender systems and relevant approaches such as collaborative filtering, matrix factorization techniques, multi-armed bandits etc.
Experience in building and deploying practical recommender systems leveraging big data sources using technologies such as Apache Spark, Neo4J and so on
Excellent programming skills in a language such as Java or Python
Experience in managing Hadoop / Spark / Mesos clusters
Experience in data analysis using software such as R or MATLAB

Benefits

Health Care Plan (Medical, Dental & Vision)
Retirement Plan (401k, IRA)
Paid Time Off (Vacation, Sick & Public Holidays)
Family Leave (Maternity, Paternity)
Training & Development
Stock Option Plan

Apply

Apply at Medeloop

About the Company

Medeloop

AI-driven platform for clinical research management

Medeloop is an AI-driven platform that speeds up clinical and public health research. It serves researchers, pharmaceutical companies, and healthcare providers by covering the full research lifecycle—from generating hypotheses and helping with funding to managing studies and analyzing data. The platform is accessed through subscriptions and includes tools like two-way messaging for patient-care team communication, advanced analytics to identify biomarkers and drug targets, and features for grant writing and scientific publication. Medeloop integrates and harmonizes large health data sources to streamline research workflows. This helps shorten discovery timelines and aim for better patient outcomes. The company operates in health-tech with a focus on early-stage clinical research and trials, positioning itself as a comprehensive, data-driven research platform rather than a collection of isolated tools.

Company size: 11-50
Company stage: Series A
Total funding: $23.5M
Headquarters: San Francisco, CA
Founded: 2021

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