Data First Jobs

Insight Global

Senior Machine Learning Engineer

Contract · Remote · USA

$70,000–$90,000 · Posted Jul 7, 2026

Work Options
Seniority Level
Cloud Stack
Job Type
Position Group
  • Senior AWS Machine Learning Engineer
  • 18-24 month contract + extensions, possibility to go permanent in the future
  • Remote- working hours in CST

Requirements:

  • - 10+ years with expertise as a Machine Learning Engineer on the ML Infrastructure/ML System side in AWS (back end engineer)
  • - SQL, experience using SQL platforms and writing SQL queries
  • - AWS - Processing data using AWS S3 and Spark or Python Pandas
  • - Know the difference between page data and streaming data processing - Build high performing APIs
  • - Build systems that read and convert data into inputs to ML models
  • - Build software systems used to train and evaluate ML models all within AWS
  • - Ability to give examples around things they have built ML pipelines such as: services, search systems, recommender systems, fraud detection.
  • - Master’s in Applied Science or PHD in Applied Science

Plus:

  • - Snowflake
  • - Exposure to Applied Science work

D2D:

  • This global hotel company is looking for a very skilled Machine Learning Engineer on the Systems/Back end/Infrastructure side. This ML Engineer will build software which uses machine learning on a typical back end application. They will build the following: high performance APIs, systems that read and convert data to inputs to ML models, and software systems used to train and evaluate ML. We need someone who has done some of the following: built ML pipelines, services, search systems, recommender systems, fraud detection. Project examples could be:
  • - Specific projects the team is working on: feature store database used to store data being use by ML models (if you go to Amazon, they know who you are, they know our profile, our purchases, compare to people in similar geographic regions using an array of data by feeding your amazon login and retrieve data representing your profile). (Feature Store Rebuild)
  • - To make models useful, you have to train them through big amounts of data. There are dozens of these models and simplify the data to be used by the models.
  • - Search on the website, recommender systems. Create elements of search engines.

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Senior Machine Learning Engineer

Insight Global

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