AI​/ML Engineer Intern at Melotech

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

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  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

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