Original listing text, shown exactly as published by the company.
What You Will Do
- Design, build, and release AI products/features that solve real user problems
- Design and implement streaming/batch data pipelines to support training and inference
- Write production-ready code and move it to production with the help of cloud services and CI/CD techniques
- Continuously monitor and improve the quality and performance of the systems
- Learn by doing — while owning real problems and demonstrating end-to-end ownership throughout the system
- Build resilient, performant pipelines and services in production
- Own system architecture, performance, observability, and scalability
- Collaborate across teams to turn ideas into impactful products
- Mentor engineers and help set technical direction
What You Will Need
- if you feel like you are one of the below;
Senior Data Scientist (NLP, Search, Recommendation)
- Designed and deployed search, ranking, or recommendation systems
- Improved core product discovery metrics (e.g. CTR, conversion, etc.) through AI/ML systems
- Worked with state-of-the-art NLP and Information Retrieval techniques such as transformers, embedding models, vector search, LLMs, RAG, agents, etc.
- A/B tested the impact you have provided and took actions to make it better
Senior Data Engineer
- Built high-throughput real-time or batch ETL pipelines (Spark, Kafka, etc.) supporting ML training/inference
- Managed data orchestration with Airflow or similar frameworks
- Built and scaled feature stores, data lakes, or analytical platforms
- Focused on data quality, wrangling, lineage, and monitoring
- Hands-on with AWS/GCP, Docker/Kubernetes, or MLOps pipelines
Senior Software Engineer
- Have strong experience with software architecture and domain-driven design to convert business ideas into working software products
- Designed and implemented robust backend systems for high loads using Java, Python, Go, or similar languages
- Worked on APIs, microservices, or messaging queue systems to serve high loads smoothly
- Integrated observability, security, and CI/CD pipelines into the systems
- Optimized data indexing, inference speed, API latency, or deployment workflows
- Hands-on experience in container systems, cloud services, infrastructure-as-code, and incident-free release of software services (Docker, Kubernetes, AWS, Terraform, feature toggles, blue-green deployment, etc.
