Rotate across three machine learning engineering teams, gaining exposure to different technical disciplines
Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
Ship production ML systems and features that impact millions of real users
Leverage cutting-edge AI technologies to build technical solutions at scale
Define success metrics and evaluate impact via offline and online experiments
Monitor, evaluate, and optimize the performance of deployed models
Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement
Participate in mentorship, technical training, and networking events throughout the program
Engage in engineering-wide initiatives to improve processes and technical standards

Requirements

Master’s or PhD degree in Computer Science or a related field with emphasis on Machine Learning
Strong coding proficiency in at least one programming language (e.g., Python, Java, C++, Go)
Solid understanding of computer science fundamentals, algorithms, and system design
Demonstrated understanding and interest in machine learning engineering
Passion for problem-solving, collaboration, and building scalable systems
Strong communication skills and ability to work in a team-oriented environment
Up to three years of combined professional experience, including internships (preferred)
Familiarity with cloud platforms (AWS, GCP, Azure) and DevOps tools (preferred)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningML pipelinesdata extractionfeature developmentmodel trainingmodel testingmodel deploymentcoding proficiencyalgorithmssystem design

Soft Skills

problem-solvingcollaborationcommunicationteam-orientedmentorshiptechnical training

Certifications

Master’s degreePhD degree