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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning models, particularly in recommendation systems and ranking algorithms. Proficient in Python and container orchestration with Docker and Kubernetes, with a strong focus on optimizing performance and conducting A/B testing.
Highest-signal resume keywords
Machine Learning Systems DevelopmentPython ProgrammingRecommendation SystemsA/B Testing ExperienceContainerization and Orchestration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningPythonSQLRecommender SystemsA/B TestingVector DatabasesFastAPIStreaming SystemsGradient BoostingLightweight Classifiers
Tools & Technologies
DockerKubernetesQdrantFAISSAnnoyScaNNHNSWlibClickHousePulsarKafka
Industry Keywords
E-commerceSearch RelevanceMarketplaceProduct DiscoverySession-Based Embeddings
Tech Stack
Tools & technologiesDockerKafkaKubernetesPulsarPythonPyTorchScikit-LearnSQL
About the role
Key responsibilities & impact- Own the machine learning powering product discovery for merchants
- Train, build, deploy, and operate models and their serving services end to end
- Design, train, and ship recommendation and ranking models, including session-based embeddings, collaborative filtering, and content and visual embeddings
- Build and operate low-latency Python services serving the models
- Run batch pipelines that rebuild model artifacts daily across hundreds of merchants
- Design and analyze A/B tests and make shipping decisions based on evidence
- Own deployments, including containers, Kubernetes manifests, autoscaling, dashboards, and alerts
- Extend the shopping assistant and LLM-assisted catalogue enrichment with proper evaluation
- Optimize live storefront recommendation re-ranking under a 200 ms budget, measured by merchant conversion
Requirements
What you’ll need- 5+ years building machine learning systems, with at least 3 years of models serving live production traffic
- Strong Python; FastAPI or equivalent
- Experience with vector databases, especially Qdrant
- Familiarity with ANN libraries including FAISS, Annoy, ScaNN, or HNSWlib
- Hands-on recommender systems or search ranking experience, including implicit feedback, embeddings, and approximate nearest neighbour search
- Solid SQL against analytical stores; ClickHouse experience is a plus
- Comfortable with Docker and Kubernetes, and willing to own deployment of your work
- Experience running A/B tests and reporting results
- Experience with streaming systems such as Pulsar, Kafka, or Flink
- Bonus: PyTorch, sentence-transformers, CLIP, or computer vision applied to product imagery
- Bonus: Gradient boosting for ranking with XGBoost, LightGBM, or CatBoost
- Bonus: Scikit-learn and SciPy for lightweight classifiers and experiment statistics
- Bonus: LLM application work with evaluation harnesses, tool calling, and cost and latency tuning
- Bonus: E-commerce, search relevance, or marketplace background
- Bonus: Python async experience
- Bonus: Experience around search