Applied Machine Learning Engineer

Company Description

XTEN-AV is the worlds first AI-powered professional AV design platform that revolutionizes the design and documentation process for audiovisual professionals. By automating multiple types of drawings and documents, XTEN-AV empowers AV designers, integrators, and manufacturers to enhance their efficiency and productivity by up to 10 times. The platform aims to streamline workflows and replace traditional design methods with cutting-edge, intelligent solutions.

About the Role

We are looking for a hands-on Machine Learning Engineer who has worked on real-world ML problems end-to-end from understanding ambiguous problem statements to building and iterating on practical solutions.

This role focuses on applied machine learning, where problem understanding, data handling, and modeling approaches are more important than using pre-built AI tools or frameworks.

Key Responsibilities

• Understand and break down ambiguous problem statements into solvable ML approaches
• Design, train, and evaluate machine learning models on real-world datasets
• Perform data preprocessing, feature engineering, and exploratory analysis
• Iterate on models based on performance, constraints, and feedback
• Build lightweight APIs/services (Python-based) to integrate ML solutions into product workflows
• Collaborate with engineering and product teams to deploy usable ML systems
• Write clean, maintainable, and testable code

Required Skills & Experience

• 4+ years of experience in Machine Learning / Applied ML roles
• Strong understanding of ML fundamentals: Supervised & unsupervised learning, Feature engineering, Model evaluation and validation
• Proven experience working on real-world datasets (not just academic projects)
• Strong Python programming skills (must be able to code independently)
• Experience dealing with messy, incomplete, or noisy data
• Ability to approach problems analytically and iterate towards solutions
• Experience building simple APIs or pipelines for ML models

Nice to Have

• Experience with computer vision, document processing, or structured data extraction
• Exposure to deep learning techniques (CNNs, transformers, etc.)
• Familiarity with experimentation workflows and model iteration
• Basic understanding of system design and scalability
• Experience working in product-based or startup environments

What Were Looking For

• Strong problem-solving and analytical thinking ability
• Someone who understands problems deeply before jumping to solutions
• Ability to work on open-ended problems where solutions are not predefined
• Ownership mindset and accountability for outcomes
• Ability to balance experimentation with practical implementation

What Youll Build

• Data-driven ML solutions integrated into real products
• Systems that improve decision-making and automation
• Scalable ML components that work reliably in production