Senior Machine Learning Engineer

About Arena Club 

If you’re fascinated by sports cards and memorabilia, your search ends here. Arena Club is pioneering the collectibles domain by introducing the first-ever digital card show. Spearheaded by 5x World Series Champion Derek Jeter and serial entrepreneur Brian Lee, Arena Club has developed a fully digital marketplace. This innovative platform is built on trust, transparency, and fun, featuring grading & authentication, vaulting, and digital pack openings for collectors to build and showcase their collections in a personalized online showroom from anywhere in the world. 

About The Role

We're looking for a Senior Machine Learning Engineer with strong Data Science experience to own end-to-end ML solutions at Arena Club. You'll combine hands-on model building — especially in computer vision — with experimentation and behavioral analysis while taking models all the way into production. You'll work cross-functionally to translate business and operational needs into ML-driven solutions that directly impact grading accuracy, product experience, and marketplace efficiency.

What You Will Do

Computer Vision for Card Grading

• 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.

Applied Data Science & ML Modeling

• 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.

Data Infrastructure & ML Operations

• 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.

Cross-Functional Partnership & AI-Accelerated Development

• 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.

Qualifications

• 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.

The Arena Club Standard

Life at Arena Club isn’t for the faint of heart — and that’s by design. We’re building products and experiences the collectibles world has never seen. This is a proving ground. It demands your best every single day, because anything less means you’re falling behind.

From day one, you’re in the game. Trusted to deliver, expected to own outcomes, and driven to raise the bar higher than you thought possible. We don’t just execute — we innovate, compete, and win together. 

If you want routine or predictability, you won’t find it here. But if you’re ambitious, relentless, and hungry to prove yourself on a team built to dominate — step into the arena. You’ll discover growth and reward here, unlike anywhere else.