Responsibilities
- Design the hybrid search infrastructure combining Elasticsearch for lexical matching, vector search for semantic understanding, and intelligent ranking layers.
- Decide how to index, query, and serve results across thousands of stores and millions of queries.
- Select and fine-tune embedding models for semantic search.
- Implement query understanding and intent classification systems.
- Train learning-to-rank models on real merchant data.
- Work at the intersection of traditional information retrieval and modern machine learning.
- Architect systems to maintain millisecond-level latency during daily and peak e-commerce periods, including 10-50x load spikes during flash sales.
- Collaborate with Product Managers and Data Scientists to implement and fine-tune search relevance algorithms including NLP components, stemming, ranking logic, and personalization features.
- Build feedback loops, A/B testing infrastructure, and measurement systems to continuously improve search.
- Establish, document, and promote engineering best practices including RESTful API standards for resource modeling, versioning, response structure, validation, and authentication.
- Set technical standards for search including API contracts, data models, and system boundaries.
- Mentor senior developers and provide guidance to the back-end development team.
- Participate actively in code reviews, offering insightful and actionable feedback.
Requirements
- Bachelor's degree in Computer Science, Mathematics, Engineering, or a related field
- 8+ years of hands-on professional back-end software engineering experience with a proven track record of delivering complex projects
- Deep Elasticsearch expertise — operated Elasticsearch at scale, understand analyzers, tokenizers, scoring, and BM25
- Vector search & embeddings experience — shipped embedding-based search in production, understand tradeoffs between architectures, dimensionality, quantization, and ANN algorithms
- Expert proficiency in Golang (Go) and other relevant back-end languages/environments (e.g., PHP)
- ML in production — ability to read papers, evaluate approaches, and implement ML systems at scale
- Experience with learning-to-rank, query understanding, or recommendation systems
- Experience with cloud computing platforms (e.g., AWS, GCP, or Azure)
- Familiarity with containerization (Docker, Kubernetes)
- Strong knowledge of both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., Redis) databases
- Proven experience designing and implementing scalable microservices
- Understanding of data streaming technologies for real-time indexing
- Prior work with A/B testing frameworks for search relevance and feature rollouts
Nice to Have
- Master's degree strongly preferred
- E-commerce experience preferred
- Experience fine-tuning embeddings on domain-specific data is valuable
Benefits
- Fully remote work across the U.S. and Canada
- Flexible vacation policy
- Generous holiday schedule
- Parental leave
- Sick policy
- Birthday holiday
- 100% free health, dental, and insurance for you and your family
- 401(k) retirement plans for U.S. employees
- TFSA and RRSP retirement plans for Canadian employees
- 3% contribution of gross salary regardless of location
Team
Structure: flat structure
Additional Information
- Compensation reflects cost of labor across several U.S. geographic markets
- Pay varies by work location and may depend on job-related knowledge, skills, and experience
- Application reviewed by engineers, not recruiters
- Strong internet connection required for remote work
