Senior Machine Learning Engineer

Arena Club is pioneering the collectibles domain by introducing the first-ever digital card show. They are seeking a Senior Machine Learning Engineer to own end-to-end ML solutions, focusing on computer vision and applied data science to enhance grading accuracy and marketplace efficiency.

Responsibilities:

• Design, train, and deploy computer vision models to detect trading cards, identify defects, and handle complex visual challenges (noisy backgrounds, lighting variability, border sensitivity)
• Work with object detection architectures such as YOLO or Detectron for bounding box detection and classification tasks
• Leverage OpenCV or equivalent libraries for cropping, edge detection, pixel-based operations, and image preprocessing
• Continuously evaluate and improve grading model performance in production
• Build and iterate ML models for recommendations, churn prediction, pricing/optimization, and other business problems
• Own analytical projects end-to-end: frame the problem, explore data, choose methods, build models, evaluate impact, and communicate findings
• Develop and maintain recommendation systems and personalization models using behavioral, transactional, and inventory data
• Design experiments and A/B tests to evaluate model and product changes; partner with Product and Operations to interpret results and decide next steps
• Translate ambiguous business questions into concrete data science and ML problems with clear success metrics
• Own the full ML lifecycle: data ingestion, feature engineering, training, evaluation, deployment, monitoring, and iteration
• Build and maintain batch and near-real-time data pipelines using Python and PySpark
• Write complex, production-grade SQL (joins, aggregations, time-window logic) to extract and transform data independently
• Deploy and operate ML workloads on AWS (EC2, S3, and related services); design scalable systems for inference and pipeline execution
• Improve reproducibility, experiment tracking, and observability across the ML stack
• Collaborate with backend engineers to integrate models into customer-facing systems
• Collaborate with Product, Engineering, Data, and Operations to translate high-level needs into technical problem statements
• Communicate trade-offs, model behavior, and timelines in plain language to non-technical stakeholders
• Leverage AI tools and agents throughout the ML lifecycle for code scaffolding, debugging, experiment design, and optimization
• Operate with high ownership and autonomy in a fast-moving, ambiguous environment

Requirements:

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related technical field required; Master's preferred
• 6+ years in machine learning engineering or applied data science, with a track record of shipping models to production in a consumer-facing environment
• 3+ years of Computer Vision experience
• 3–5+ years building and deploying ML models beyond POCs (e.g., recommendation systems, ranking, churn, pricing/optimization)
• Expert-level Python and ML ecosystem proficiency (Scikit-learn, PyTorch, TensorFlow)
• Strong experience with classical ML and applied modeling (tree-based methods, logistic regression, gradient boosting, embeddings/twin-tower architectures)
• Advanced SQL for complex data extraction and processing
• Experience applying ML to user behavior data (clickstream, transactional, event logs)
• Comfort with experimentation and causal inference basics (A/B testing, lift measurement, business impact interpretation)
• Experience taking data science work into production and measuring impact over time
• Strong AWS experience (EC2, S3, and related services) for model hosting and data workflows
• Knowledge of OpenCV & MLFlow