Intern – Machine Learning Engineering – Machine Learning

Job Description

Intern – Machine Learning Engineering | Scowtt

The Tone:
This is an internship at Scowtt, available as a Hybrid or Remote role. Scowtt is an early-stage startup focused on transforming business lead conversion through AI/ML marketing optimization and autonomous sales experiences. They achieve this by integrating CRM, web signals, and product interaction data into a real-time conversion model. This role is crucial for their rapid growth and for ensuring the clean onboarding of new customers, providing an excellent opportunity to learn how to develop production-grade ML products.

The TL;DR
• Role: Internship
• Location: Hybrid or Remote
• Pay: $25–$45 Hourly
• Mission: Contribute to developing production-grade machine learning products that turn interest into action for businesses.
• Tech Stack: Python, ML frameworks, cloud platforms (GCP, AWS), BigQuery, notebooks, data pipelines, Lambda, Amplify, SQS, DynamoDB, PostgreSQL

What You’ll Actually Do
• Model Development: Train, evaluate, and deploy machine learning models.
• Data Utilization: Work with structured and behavioral data used in marketing and sales systems.
• Pipeline Management: Help automate and maintain data and machine learning pipelines.
• AI Integration: Collaborate with engineers to integrate ML models into AI agents.
• Performance Optimization: Debug, optimize, and improve model and pipeline performance within production environments.

The Must-Haves
• Background: A rising senior currently studying at the undergraduate or graduate level in Computer Science, Machine Learning, Data Science, or an equivalent field from a reputed university.
• Experience: Possesses a solid foundation in computer science principles, including data structures and algorithms. Has exposure to machine learning frameworks through coursework, personal projects, or previous internships, ideally with hands-on experience using real datasets.
• Skills: Demonstrates strong proficiency in Python and familiarity with machine learning concepts and workflows. Committed to following and enforcing security best practices, including secure coding and proper handling of sensitive data, alongside an ownership mindset and eagerness to learn.
• Bonus: Experience with cloud platforms such as GCP or AWS, and familiarity with tools like BigQuery or notebooks. Exposure to basic data pipelines or AI Agent development is also beneficial. Knowledge of technologies such as Lambda, Amplify, SQS, DynamoDB, or PostgreSQL is a plus.