Machine Learning Engineer

Laivly is a division of 24-7 Intouch that specializes in emerging technology. Our team of data scientists, developers, and researchers are dedicated to creating unique solutions by partnering smart technology with friendly humans to maximize the potential of customer service interactions. We empower human potential with artificial intelligence and machine learning to enhance quality, empathy, and productivity in measurable ways for the benefit of the brands and consumers we serve.

24-7 Intouch is a global contact center & technology company that delivers innovative and value-driven customer service solutions across all industries via an omnichannel approach that includes voice, social media management, live chat, email, fraud, UAT, self-service, and back office administration. Using the most advanced technology in the industry, 24-7 Intouch’s customizable customer care platform allows their clients to utilize business insights to deliver lifetime consumer loyalty and increase incremental revenue. With over 19 years of experience, the 24-7 Intouch team takes pride in building a top to bottom brand alignment for partners to create exceptional customer experiences.

About the Job
We are working with the world’s biggest brands to invent the future of customer service and we need your help!

We are seeking Machine Learning Engineers to design, develop, and implement special projects within 24-7 Intouch’s product development division, Laivly. We’re looking for an entrepreneurial mindset, a fearless attitude, and a passion to take Machine Learning to the next level. Our team not only takes pride in our work, but also in the way we do it. If you’re a creative, daring scientist, we are the place for you. 

As Machine Learning Engineer, You Will…
  • Understand business objectives and develop models/tools required to achieve within a proprietary machine learning platform
  • Analyze and experiment with ML algorithms to determine viability to solve a given problem
  • Tune model hyperparameters for best performance
  • Document and explain the results of experiments for relevant stakeholders
  • Gather data and manipulate it, including verifying/ensuring data quality, and defining pre-processing or feature engineering
  • Work in a fast paced agile software development environment
  • Have fun building Skynet to take over humanity. (We're joking, of course.)
  • Develop machine learning models to handle business challenges in customer service ranging from email, chat, compliance and voice applications
  • Bring your ideas and try new things as part of a research focused mindset
  • Eat free snacks and drinks. Yum!

As Machine Learning Engineer, You Have…
  • Bachelor’s degree/equivalent in Computer Science, Mathematics, Statistics, Engineering or similar with practical experience in machine learning development
  • Significant experience with data integration and reporting is preferred
  • Experience with NLP (Natural Language Processing) techniques is preferred
  • Experience in solution design and implementation is preferred
  • Experience in Continuous Integration/Automated Deployment is preferred
  • Experience working with Agile software development methodologies is preferred
  • Proficiency with a deep learning frameworks is required (Keras, Tensorflow, Pytorch)
  • Proficiency in Python development and basic ML libraries (numpy, pandas, scikit-learn) is required
  • Great communication skills and can play nice with others
  • A fun attitude, ‘cause lets face it...boring won’t cut it
  • Passion for Machine Learning and a willingness to learn new, cutting-edge skills
  • Excellent communication skills, with the ability to coach and mentor other team members
  • Strong documentation skills and the ability to explain Machine Learning concepts and relevant experiments to stakeholders
  • A deep understanding of data, databases and data labeling
  • ...what it takes. We want you to apply now! 

Want to apply later?

Type your email address below to receive a reminder

Apply to Job

ErrorRequired field
ErrorRequired field
ErrorRequired field
ErrorRequired field