Accountabilities Actions and Expected Measurable Results

Recommendation Algorithm Development 30%

• Design and build the core personalization engine using user-saved product data as behavioral signals.

• Develop multi-signal recommendation models that incorporate brand affinity product category color palette fit/sizing signals price sensitivity and trends.

• Implement and evaluate a range of approaches including collaborative filtering content-based filtering and hybrid neural architectures.

• Build and maintain product embedding models that capture rich semantic similarity across the retailer feed catalog.

• Develop cold-start strategies to generate high-quality recommendations for new users with limited save history.

Data Ingestion & Feature Engineering 25%

• Design and maintain robust pipelines to ingest normalize and enrich product feeds from thousands of retail partners.

• Collaborate on a unified product taxonomy and attribute extraction layer that standardizes inconsistent retailer data into coherent features (category color material fit etc.).

• Leverage NLP and computer vision techniques to extract attributes from unstructured product descriptions and images.

• Partner with the data engineering team to maintain data quality freshness and catalog coverage at scale.

Personalized Feed & Ranking 25%

• Build and own the ranking and re-ranking layer that assembles each user's personalized feed in real time.

• Develop and tune multi-objective ranking that balances relevance novelty diversity and business goals (e.g. promoted/sponsored retailer partnerships).

• Implement feedback loops that continuously update user preference models based on implicit signals (saves clicks dwell time shares).

• Build A/B testing solutions to rigorously evaluate ranking and recommendation changes against key engagement metrics.

Engineering Excellence 20%

• Own production systems. Debug issues across indexing retrieval ranking and serving layers

• Create clear documentation for pipelines models APIs and system design.

• Contribute to best practices for ML systems API design and scalable infrastructure.

• Stay current with advancements in recommendation ranking and personalization systems and apply them where they make practical impact.

Education:

Bachelor’s degree in Computer Science Engineering or a related field.

Experience:

You have a strong foundation in modern backend and ML engineering practices and continue to learn and evolve. You bring:

• 5+ years of ML engineering experience focused on recommendation systems personalization or search ranking with hands-on depth in collaborative filtering matrix factorization content-based and hybrid neural approaches.

• Proven experience designing training and deploying embedding models and vector retrieval (e.g. Milvus Pinecone) for product or content similarity at catalog scale.

• Production experience serving real-time low-latency ML predictions and managing the full model lifecycle — training deployment versioning and monitoring — on cloud ML platforms such as AWS SageMaker or GCP Vertex AI (including Vertex AI Pipelines).

• Rigorous experimentation discipline: experiment design A/B and multivariate testing and the analytical ability to translate model results into clear product and business decisions.

• Extensive backend engineering with strong proficiency in Python and modern ML frameworks (PyTorch TensorFlow or JAX) plus working knowledge of Node.js and TypeScript.

• Experience designing large-scale data and feature pipelines using Apache Kafka Spark Beam Airflow or Flink for streaming ingestion transformation and feature engineering.

• Applied NLP and/or computer vision experience extracting structured attributes (category color material fit) from unstructured product descriptions and imagery.

• Strong API and infrastructure foundations: REST and GraphQL design with secure auth (OAuth/JWT) Git-based workflows containerization with Docker and Kubernetes and production observability with Grafana Kibana and APM tooling.

• Curiosity and pragmatism around emerging AI particularly LLMs and modern retrieval/ranking techniques with a track record of bringing new approaches into real production use cases.

• Strong written and verbal communication able to explain technical tradeoffs to both technical and non-technical stakeholders with a data-driven approach to problem solving.

Specific Knowledge Skills Certifications and Abilities:

Core Tech Stack

• Backend and API development using Python FastAPI Node.js and TypeScript.

• Search and indexing using Elasticsearch for relevance retrieval and query optimization.

• Event driven architecture and streaming using Apache Kafka.

• Vector search and embeddings infrastructure using vector databases such as Milvus or Pinecone.

• Cloud and infrastructure using Google Cloud Platform or Amazon Web Services with containerization via Docker and orchestration through Kubernetes.

 Travel Required (Approximate): 0%

What the Team is Saying

People Inc. Offices

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