Associate Machine Learning Engineer

Information Technology - Ontario, Canada London, Ontario


Description

Join the Data Technologies Decoder team as an Associate Machine Learning Engineer

When was the last time you bragged about where you work? At CARFAX, we do it every day. Why? Because we’re proud to work for a company with a strong mission and trusted brand. We’re proud to work with people who care about what they do and work hard every day to deliver their best. And just announced, the return of the 4-day work week from Memorial Day through Labor Day! We’ve created the type of company culture where the term, “work- hard play- hard” isn’t just a catchy saying, it’s part of the #CARFAXdifference! Even today, while working remotely temporarily due to COVID-19, our culture is strong, and our team is connected!

Offers of employment may be conditional upon providing evidence of COVID-19 full vaccination.

 As an Associate Machine Learning Engineer, you will work collaboratively on a team of engineers and data scientists to design, build and scale our ambitious machine learning and natural language processing solutions. Leveraging the latest techniques and tools, this unique opportunity will aim to unlock the hidden potential in billions of records and enrich the quality of data provided by our automotive reporting products delivered daily to consumers across the globe.

As an Associate Machine Learning Engineer you will:
  • Work in a team environment using Agile practices
  • Utilize Test Driven, Paired Programming and Continuous Integration development methods
  • Innovate new ideas to evolve our applications and processes
  • Acquire, analyze, transform and move large, complex datasets
  • Participate in the design and development of our deep learning, ML and NLP solutions
  • Understand NLP concepts like: Named Entity Recognition (NER), Sentiment Analysis, Data Tokenization, Lexical Semantics, Relationship Extraction, etc.
  • Understand ML fundamentals: loss functions, classification and regression models, feature engineering, hyperparameter optimization, and model validation
To be successful as an Associate Machine Learning Engineer, you will be expected to have:
  • Master's or Bachelor’s degree in Computer Science, Data Science, Mathematics, (or related field of study)
  • Software engineering skills: version control, build pipelines, object-oriented programming.
  • Familiarity with database concepts
  • Ability to solve complex problems and learn from mistakes
  • Relational and NoSql database systems experience


Preferred Experience:

  • ML frameworks: TensorFlow, Torch, Caffe, or Theano.
  • Python, Scala, Java or .NET.
  • Oracle, MySQL, PostgreSQL or MongoDB
  • Hadoop/AWS EMR, Apache Spark
  • AWS product knowledge (e.g. EC2, VPCs, Lambda, API Gateway, etc.)
  • AWS Console, CLI and SDK experience
  • Infrastructure as code: Terraform, CDK, or CloudFormation
  • Git version control

About CARFAX

CARFAX, a unit of IHS Markit (NYSE: INFO), helps millions of people every day confidently shop, buy, own and sell used cars with innovative solutions powered by CARFAX vehicle history information. The expert in vehicle history since 1984, CARFAX provides exclusive services like CARFAX Used Car Listings, myCARFAX, CARFAX History-Based Value and the flagship CARFAX Vehicle History Report. CARFAX is a nationally recognized top workplace by The Washington Post and Glassdoor.com. Based in London, IHS Markit is a world leader in critical information, analytics and solutions.

CARFAX is an Affirmative Action/Equal Opportunity Employer. It is the policy of CARFAX to provide equal employment opportunity to all persons regardless of race, color, sex, pregnancy, religion, national origin, age, ancestry, citizenship status, veteran status, military status, disability or handicap, sexual orientation, genetic information or any other status protected by federal, state or local law. In addition, CARFAX will provide reasonable accommodations for qualified individuals with disabilities. We maintain a drug-free workplace. We are a participant in E-Verify.