Note: The job is a remote job and is open to candidates in USA. People Inc. is focused on building a next-generation product discovery platform that connects shoppers with their desired products. As a Senior Software Engineer for personalization, you will design and develop the recommendation algorithm that enhances user experience by personalizing their product feeds based on individual preferences.
Responsibilities
- 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
- 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
- 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
- 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
Skills
- Bachelor's degree in Computer Science, Engineering, or a related field
- 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
- 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
Benefits
- Annual bonuses
- Short- and long-term incentives
- Medical, dental, vision, prescription drug coverage
- Unlimited paid time off (PTO)
- Adoption or surrogate assistance
- Donation matching
- Tuition reimbursement
- Basic life insurance
- Basic accidental death & dismemberment
- Supplemental life insurance
- Supplemental accident insurance
- Commuter benefits
- Short term and long term disability
- Health savings and flexible spending accounts
- Family care benefits
- A generous 401K savings plan with a company match program
- 10-12 paid holidays annually
- Generous paid parental leave (birthing and non-birthing parents)
- Voluntary benefits such as pet insurance, accident, critical and hospital indemnity health insurance coverage, life and disability insurance
Company Overview