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Sign up with Clera and we'll reach out the moment a role actually fits you — no more spraying applications into the void.
$170k–$230k/yr
Full-time
bachelor degree, postgraduate degree
Bonus, Equity
Posted 23d ago
~40 hrs/week
Responsibilities
Own the end-to-end lifecycle of recommendation and ranking systems to improve product discovery and conversion across the marketplace. Establish methodological standards for data science and mentor new team members in a player-coach capacity.
Requirements
Requires a Bachelor's degree in a quantitative field and 5+ years of applied ML experience, including 3+ years specifically in recommendation or personalization systems. Proficiency in Python, the ML ecosystem, and AWS is essential.
Full job description
About the Role
We're seeking a Senior Machine Learning Engineer to own the systems that determine how products are surfaced, ranked, and discovered across Arena Club's marketplace, spanning search relevance, ranking, and personalization on every surface and category. This is production machine learning in the request path, where model quality translates directly into conversion, engagement, and revenue. The mandate covers the full lifecycle: taking a ranking approach from concept through deployment, monitoring, and continuous iteration, then validating each improvement in rigorous controlled experiments. Impact here is measured not by models shipped but by demonstrable lift in the metrics that move the business. This is a player-coach role. Beyond building models directly, you will establish the methodological standards and rigor for data science across the organization and mentor new Data Scientists who join as the team grows.
What You'll Do
Recommendations & Ranking
Design, train, and deploy recommendation and ranking models that improve relevance and conversion across the marketplace
Own a recommendation and search index that beats a strong third-party control on conversion and cuts fallback rates
Build homepage, onboarding, and category-level personalization ranking
Develop retrieval and candidate-generation systems using embeddings and semantic search
Reuse cross-domain features such as item scores and tier weights as ranking signals across surfaces
Production ML & ML Operations
Own the full ML lifecycle: data ingestion, feature engineering, training, evaluation, deployment, monitoring, and iteration
Deploy and operate low-latency inference in the request path, and design scalable systems for serving and pipeline execution
Build and maintain batch and near-real-time data pipelines using Python and PySpark
Deploy and operate ML workloads on AWS (EC2, S3, and related services)
Improve reproducibility, experiment tracking, and observability across the stack
Collaborate with backend engineers to integrate models into customer-facing systems
Cross-Functional Partnership & AI-Accelerated Development
Partner with Product, Engineering, and the marketplace squad to translate high-level needs into technical problem statements
Design and interpret A/B tests to validate model and product changes
Communicate model behavior, trade-offs, and timelines in plain language to non-technical stakeholders
Leverage AI tools and agents throughout the ML lifecycle for code scaffolding, debugging, and optimization
Operate with high ownership and autonomy in a fast-moving, ambiguous environment
Qualifications
Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative field; advanced degree preferred
5+ years in applied ML or ML engineering, with a track record of shipping models to production in a consumer-facing environment
3+ years building recommendation, ranking, search relevance, or personalization systems
Expert-level proficiency in Python and the ML ecosystem (PyTorch, TensorFlow, Scikit-learn)
Experience with learning-to-rank, embeddings, retrieval, and semantic search
Advanced SQL for complex data extraction and processing.
Experience applying ML to user behavior data (clickstream, transactional, event logs)
Strong AWS experience (EC2, S3, and related services) for model hosting and data workflows
Comfort with experimentation (A/B testing, lift measurement, business impact interpretation)
Knowledge of MLFlow or an equivalent experiment-tracking and model-registry tool
Preferred Qualifications
Experience with AWS OpenSearch, Algolia, or other search and retrieval systems
Experience with Marketplace recommendation system modeling
Experience with vector databases and approximate nearest-neighbor search
NLP experience, including embeddings, text classification, or semantic search
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. That's how real breakthroughs happen.
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.
The base salary range listed is a guideline. Actual compensation is determined based on skills, experience, and the impact you bring. Total compensation includes base salary, bonus, and equity.
We’re building products and experiences the collectibles world has never seen.
Industry
Technology, Information and Internet
Company size
51-200 employees
Founded
2021
Headquarters
Los Angeles, CA
LinkedIn followers
28,194
Total funding
$10M
At Arena Club, we’re igniting a collectibles revolution. Backed by legendary 5× World Series Champion Derek Jeter and trailblazer Brian Lee, we’ve launched the first-ever digital card show—a dynamic marketplace where innovation, transparency, and pure excitement drive everything we do. Our cutting-edge platform offers unparalleled grading, authentication, secure vaulting, and digital pack openings (Slab Packs™), giving collectors the power to curate unique online showrooms and redefine their collecting experience.
Offices: Los Angeles, CA, US · London, GB · New Delhi, IN · Beaverton, OR, US
CollectiblesMarketplaceand Trading CardsSportsGamingCard and Board GamesMarketplaceInternetMachine Learning