People Inc. · 1 month ago
Senior Software Engineer 1, ML
United States
Full-time
Remote
Senior Level
$170K/yr - $195K/yr
5+ years expPeople 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.
AdvertisingSocial MediaDigital MediaE-CommerceEducationTelecom & CommunicationsInternet of ThingsPublishingCommunitiesContentInternet
No H1B
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
Qualification
Required
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
People Inc.
People Inc. is a digital media company that specializes in research, technology, finance, operations, and consumer services. It is a sub-organization of IAC.
1001-5000 employees
Funding
Current Stage
Late StageTotal Funding
unknown2012-08-26Acquired
1998-01-01Series Unknown
Leadership Team
Neil Vogel
CEO
Nabil Ahmad
Chief Technology Officer
Recent News
2026-07-11
2026-07-11
Company data provided by crunchbase