Intern

Posted 4 Days Ago
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Chicago, IL
Internship
Information Technology • Logistics • Software
We make a unified map designed for every moving vehicle
The Role
As an ML Research Engineer Intern, you will assist in algorithm design and implementation, analyze large data sets, and derive insights from sensor data. You'll work on developing machine learning models and creating Proof of Concept applications.
Summary Generated by Built In

What's the role?
As a ML Research Engineer Intern, you will work with outstanding engineering team of Data Engineers, Data Scientists and Research Engineers to gain hands-on experience in deriving insight from various data pipeline for Multi-Industry Solutions organization. You will assist in the design and implementation of algorithms, helping to structure, analyze, and leverage sensor data for various applications. As part of our dynamic team, you'll have the opportunity to contribute to the design and development of machine learning models and tools that drive innovation and improve quality across products related to Places POI, Logistics and last-mile delivery, real-time traffic, incidents, routing and ETA in multi-modal transportation system.
Key Responsibilities:

  • Assist in the development and programming of machine learning algorithms for new product innovation and product quality improvement.
  • Support the analysis of large data sets and translation of machine learning models into code and their integration into systems.
  • Conduct exploratory data analysis on various automotive sensor data and derive insights for feature engineering and model choices.
  • Build Proof of Concept applications that implements state of the art ML algorithms.


Who are you?
You have a strong engineering background, or any quantitative field and you are self-motivated and enthusiastic about learning/applying new technologies.

  • MS or PhD student in a discipline such as Statistics, Computer Science or Engineering.
  • Basic knowledge of Machine Learning, data mining and analytic methods such as regression, classifiers, Clustering, Probabilistic Graphical Models, Deep Learning and Graph Neural Networks, etc.
  • Programming experience with Python, C++, Javascript, SQL, Scala, Julia, R, etc.
  • Knowledge of developing with AWS EMR, EC2, S3, Lambda, etc.
  • Experience working on GNSS probe sensor data or computer vision data is a plus


HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics.
#LI-AG3
Who are we?
HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes - from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.
If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.

Top Skills

C++
JavaScript
Julia
Python
R
Scala
SQL

What the Team is Saying

Alok
Katherine
Vrushali
The Company
HQ: Amsterdam
9,000 Employees
Hybrid Workplace
Year Founded: 1985

What We Do

HERE Technologies is a location data and technology company that created the first digital map over 35 years ago. Today we are the world's leading location platform company with a global footprint across 52 countries. Although our strongest presence is in the automotive industry, we also work with leading companies across a wide range of industries, including transport and logistics, mobility, manufacturing and retail and the public sector.

Why Work With Us

At HERE, we're always excited about discovering people who share our passion for building innovative solutions that make the world easier to navigate. We believe our success is powered by our team's diversity, creativity and collaboration and we're always looking for opportunities to grow it further.

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HERE Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Typical time on-site: Not Specified
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