Machine Learning / AI Engineer

Basic Qualifications:

• BS in Computer Science (or related field) with 5+ years of experience in machine learning or related fields
• 2+ years of AI experience, preferably in an e-commerce environment
• Strong theoretical understanding of modern machine learning approaches, including deep learning architectures, generative models, and large language models
• Experience with both computer vision techniques and natural language processing
• Experience fine-tuning or training models for specific applications
• Experience deploying models into production for customer use
• Familiarity with cloud services, specifically AWS, for deploying and scaling machine learning models
• Familiarity with relevant libraries and frameworks for developing, fine-tuning, and evaluating models.
• Yolov8, Pytorch, SAM, YOLO-NAS, vLLM
• Strong skills in designing evaluation pipelines and optimizing system performance
• Ability to analyze data, identify patterns, and make data-driven decisions
• A strong initiative on keeping up to date with emerging tools and technologies
• Proven track record of managing complex machine learning projects and collaborating with cross-functional teams
• Excellent communication, collaboration, and leadership skills
• Customer-focused mindset

Key Responsibilities:

• Design, implement, and maintain machine learning algorithms and models for diverse applications
• Work with expansive datasets, programming languages, and AI frameworks
• Architect and develop scalable pipelines that integrate multiple AI techniques
• Execute various stages of the project pipeline, including data preprocessing, model selection, and deployment
• Maintain and optimize infrastructure supporting machine learning workflows
• Build systems for evaluating model performance and optimizing solutions
• Collaborate closely with data scientists, software engineers, and product managers
• Document processes and systems to ensure knowledge sharing and maintainability
• Analyze and interpret complex data sets to drive business insights and recommendations

Areas of Focus

• Generative AI Applications: Creating systems leveraging diffusion and transformer-based models for artwork creation, mockup generation, and product visualization
• Artwork Enhancement Optimization: Developing pipelines that automatically improve and optimize customer-uploaded designs for print quality
• Customer Experience Enhancement: Building intelligent interfaces and recommendation systems that understand customer needs and preferences
• Digital-to-Physical Translation: Developing workflows that ensure customer designs translate perfectly when printed by detecting and resolving potential printing issues
• Intelligent Search Discovery: Creating semantically-aware systems that help customers find products based on intent, context, and behavior beyond simple keyword matching
• Multi-modal Integration: Combining visual, textual, and behavioral data to create comprehensive customer solutions that work across different interfaces
• Predictive Analytics: Developing models for trend detection, inventory forecasting, customer behavior prediction, and intelligent search suggestion/completion systems
• Real-time Processing Pipelines: Building scalable systems that can handle complex workflows with high throughput requirements

About RYZ Labs:

Our values and what to expect:

• Customer First Mentality - every decision we make should be made through the lens of the customer.
• Bias for Action - urgency is critical, expect that the timeline to get something done is accelerated.
• Ownership - step up if you see an opportunity to help, even if not your core responsibility.
• Humility and Respect - be willing to learn, be vulnerable, and treat everyone who interacts with RYZ with respect.
• Frugality - being frugal and cost-conscious helps us do more with less.
• Deliver Impact - get things done in the most efficient way.
• Raise our Standards - always be looking to improve our processes, our team, and our expectations. The status quo is not good enough and never should be.