Ktizo is an early-stage music tech startup that utilizes AI to streamline the music
licensing process for artists and music supervisors. We have launched our first version of our AI product and have gained substantial interest from high-quality artists and music supervisors. In order to continue our path toward product-market fit and scalability, we need an experienced Machine Learning Engineer who can take the founder’s vision and take it to the next level. The Machine Learning Engineer is the key technical leader of Ktizo and is responsible for overseeing every technical aspect of the company, from ideation to research and actual engineering of AI products. Early on, your key duties are built around managing the current AI product and identifying solutions to any areas that need improvement. We're obsessed with creating a frictionless and intuitive experience for our users, so we are reliant on a product that effectively leverages Artificial intelligence/Machine learning.
The position reports directly to the Founder/CEO. This is a contract job (not an employee), is remote work, location does not matter.
We have no benefits currently. If interested, email your resume and cover letter to email@example.com
- As a Machine Learning Engineer, you will play an integral part in driving innovation in recommendations and discovery of the current product and any future products in the pipeline
- You will work in the intersection of software development and machine learning, developing scalable ML-based music solutions
- You will work closely with the Founder to build products that bring 10x value to the music industry.
- Improve upon any existing methods by developing new data sources, testing model enhancements, and fine-tuning model parameters
- Development of audio and music analysis and processing algorithms that accelerate the music licensing process
- Design, develop, evaluate, and deploy highly scalable machine learning (ML) models
- Design, develop and maintain ML pipelines processing large amounts of structured and unstructured, complex and interrelated datasets
- Suggest, collect and synthesize requirements and create an effective feature roadmap for ML-driven improvement in personalization, recommendation, and accuracy
- Drive the long-term vision for platform and tool choices, ensuring the availability, stability, and security of solutions
- Drive system architecture and lead engineering best practices that enable a quality product
- Communicate results and advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision-makers
Qualifications / Skills
- You have a strong background in Machine Learning Engineering and MLOps and are familiar with state-of-the-art ML libraries and platforms
- You are experienced in building ML solutions at scale, using distributed systems, cloud infrastructure, and services
- You understand the workflow and life cycle of machine learning and are familiar with platforms for end-to-end production ML pipelines (e.g. Tensorflow)
- You can apply DevOps principles to Machine Learning solutions
- Familiar with KNN and CNN models
- Enjoys improving existing patterns and finding innovative solutions for new
- features and use cases
- A generalist - can wear different hats related to the product in order to propel the
- company toward success
- Self-motivated and a great communicator
- Humility and objectivity: you welcome feedback and are eager to incorporate new ideas into product iterations
- Accountable and operates with high integrity
- Creative problem-solving skills and change agent.
- A risk taker - willing to be bold and swing for the fences!
- You’re comfortable with change and uncertainty, as the startup world requires consistent pivoting and adjustment
- Strong work ethic with a high degree of energy
- Demonstrates the ability to show up on time and prepared
- Must be a quick learner
- Resourceful: if you don't know the answer, you know how to find out!
Education & Experience Requirements
- Great if you have a passion for music, but this is not a requirement
- Bachelor’s degree or equivalent experience in Computer Science or a related field
- 3+ years of machine learning engineering – or equivalent – designing large-scale machine learning systems from existing data science/machine learning models
- 2+ years of experience contributing to the system design or architecture (architecture, design patterns, reliability and scaling) of new and current systems
- 3+ years of programming experience
- Strong hands-on experience programming in Python and familiar with FastAPI frameworks