Senior Machine Learning Engineer, Recommendations
About The Position
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.
Requirements
- 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
Nice To Haves
- 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
Responsibilities
- 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
- 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
- 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
Benefits
- Total compensation includes base salary, bonus, and equity.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
