Job description

WHO WE ARE Melotech is revolutionizing media and entertainment. We create art through technology for humans to enjoy. In just 24 months, our work has been heard, watched and loved for over 4 billion minutes worldwide. Founded by entrepreneur and investor Soheil Mirpour, Melotech has raised more than $16M from Drive Capital and Europe’s leading VCs: Cherry Ventures, Speedinvest and Global Founders Capital, alongside world-class angels from firms such as Spotify, Blackstone and KKR. WHAT YOU WILL DO As our ML Engineer, 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 right architecture for training and deploying models that learn from real time data? 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 within our content and product ecosystem - Designing scalable ML infrastructure and pipelines that handle massive media datasets - Implementing efficient inference systems across our products - 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 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 WHAT MAKES THIS EXCITING You are one of the first employees of an ambitious team, changing the world of media and entertainment. Being early means every decision you make shapes our trajectory. You're not a cog in the machine but the captain of your own ship, rewarded for performance and respected for leadership. Flat hierarchies mean that your voice matters, your ideas get implemented, and your impact is immediate. We pay competitive salaries and make you an owner of the business with equity. We work remotely to give you complete freedom over your life, while meeting regularly around the world for global offsites where we strategize, bond, and push boundaries together. WHAT THE PROCESS WILL LOOK LIKE We hire on a rolling basis. Earliest starting date is always ASAP. Once you begin our process, you can progress from start to offer within a week, depending on how quickly you can move through each stage: 1. Take-home case study: Real-world project - showcase your skills and working style 2. Case interview: 90-minute case discussion - getting to know you & present and debate your results with a team member 3. Online assessment: Motivational questionnaire and aptitude test - are you made for the job? 4. Founder interview: 90-minute interview with our CEO - going deep on all topics 5. Team interview: Individual or group interview with other team members - depending on position 6. Offer, contract signing and onboarding Note: As we are still in stealth, you will learn more about Melotech as you progress through the stages. By the end of the Founder interview, you will have a full grasp of our business and the details of your role.

Requirements

Must-haves

Building and deploying production ML models within our content and product ecosystem
Designing scalable ML infrastructure and pipelines that handle massive media datasets
Implementing efficient inference systems across our products
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
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
We hire on a rolling basis. Earliest starting date is always ASAP.
Once you begin our process, you can progress from start to offer within a week, depending on how quickly you can move through each stage:
Take-home case study: Real-world project - showcase your skills and working style
Case interview: 90-minute case discussion - getting to know you & present and debate your results with a team member
Online assessment: Motivational questionnaire and aptitude test - are you made for the job?
Founder interview: 90-minute interview with our CEO - going deep on all topics
Team interview: Individual or group interview with other team members - depending on position
Offer, contract signing and onboarding

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaimachine learningpythondockerkubernetes

Platform: ashby
Effort: quick