AI/ML Engineer Intern at Melotech

Feedinkoo; United States; Remote; USD 40,000 - 70,000; Part time

Benefits offered by this job

Salary + equity

Remote work with offsites

Job summary

Feedinkoo is seeking an ML Engineer Intern to serve as a technical backbone for our content platform, tackling scalable ML systems and low latency challenges while leveraging cutting-edge models. You’ll work autonomously with our founder and team on production ML models, scalable pipelines, multimodal inference, and API integration.

You will gain hands-on experience in real-time cultural trend understanding, deployment, monitoring, and cross-team collaboration in a fast-paced environment.

Qualifications

• Production ML engineering experience building production systems.
• 3+ years hands-on ML engineering experience.
• Expert-level proficiency in Python, ML frameworks, and cloud platforms.
• Extensive experience with MLOps tools including Docker, Kubernetes, model versioning, and monitoring.
• Proven track record deploying and scaling ML models in production environments with high availability.
• Self-directed approach with ability to architect complex systems independently while collaborating across teams.

Responsibilities

• Build production ML models powering the content platform.
• Design scalable ML infrastructure and data pipelines.
• Implement inference systems for multiple verticals.
• Fine-tune and deploy multimodal AI systems using MLOps practices.
• Collaborate with data science teams to productionize research models.
• Optimize model performance for cost and latency.

Skills

Production ML engineering

Python

ML frameworks (TensorFlow/PyTorch)

Cloud platforms (AWS/GCP/Azure)

System design for scalable ML

Self-directed / independent architect

Team collaboration

Fast-paced environment

Education

Bachelor's degree in Computer Science, ML, Mathematics, Engineering, or related field

Tools

Docker

Kubernetes

Model versioning

Monitoring systems

APIs development

Job description

What You Will Do

As our ML Engineer Intern, you’ll be the technical backbone powering our content platform. You’ll tackle the critical questions: How do we build ML systems that scale to millions of users while maintaining low latency? What’s the optimal architecture for training and deploying models that understand cultural trends in real‑time? And how do we leverage cutting‑edge models to enhance creative processes while preserving quality? Working fully autonomously alongside our founder and the team, your answers to these questions will directly influence our company’s success. On a typical day, your tasks may include:

• Building and deploying production ML models for within our content and product ecosystem
• Designing scalable ML infrastructure and pipelines that handle massive media datasets
• Implementing inference systems for content optimization across multiple verticals
• Fine‑tuning and deploying multimodal AI systems using MLOps best practices
• Collaborating with data science teams to transition research models into production‑ready systems
• Optimizing model performance for cost efficiency while maintaining accuracy and speed requirements
• Integrating ML capabilities into existing platforms and building APIs for seamless model consumption

Who You Are

You’re a production‑focused upcoming ML engineer who bridges the gap between cutting‑edge tech and scalable systems. Your expertise lies in building robust ML infrastructure that powers real‑world applications at scale. You thrive in fast‑paced environments where your technical decisions directly impact business outcomes and user experiences. Typically, your profile will look like this:

• Degree in Computer Science, Machine Learning, Mathematics, Engineering, or related technical field
• 3+ years of hands‑on ML engineering experience building production systems at Big Tech companies, high‑growth startups, or media/entertainment platforms
• Expert‑level proficiency in Python, ML frameworks, and cloud platforms
• Extensive experience with MLOps tools and practices including Docker, Kubernetes, model versioning, and monitoring systems
• Proven track record deploying and scaling ML models in production environments with high availability requirements
• Self‑directed approach with ability to architect complex systems independently while collaborating across technical teams
• You thrive in a fast‑paced and performance‑oriented environment
• Colleagues would describe you as hard‑working, ambitious and persistent
• You’re obsessed with music, video or social media

Benefits

• Competitive salaries and equity ownership
• Remote work with complete freedom over life while meeting for global offsites