Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.
Performance and Cost Engineering: Optimize Spark and SQL jobs, file layout, partitioning, cluster configuration and workload scheduling to improve reliability, runtime and AWS/Databricks cost efficiency.
Research cutting-edge AI technologies such as AI Agent, NLP, LLM, Computer Vision, and Generative AI to precisely interpret, generate, and review ad creatives. Build interactive marketing capabilities through in-depth understanding of user intent to enhance ad experiences.
This is a start up-style role with broad ownership across AI development, data collection, robot integration, testing, failure analysis, and continuous performance improvement. The ideal candidate is comfortable working across disciplines and taking ideas from early experimentation through to real-robot deployment.
We are looking for a hands-on Embodied AI Engineer with experience in robot learning to develop, fine-tune, deploy, and validate intelligent behaviors on physical humanoid robots.
MLOps/AIOps Platform Engineering: Design and implement reusable platform components and golden paths for experiment tracking, model registry, pipeline orchestration, deployment, monitoring, feedback and retraining using Databricks, MLflow and approved AWS services.
Sales Execution & Quota: Own the technical sales cycle from discovery to close. This is a quota-carrying role where success is measured by revenue growth and solution adoption.
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Interest in or experience developing and executing automated tests, investigate and assist in problem analysis, and resolution of delivered software components
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Provide technical leadership across backend, frontend, API integration, LLM orchestration, agent frameworks, and workflow automation to ensure consistency and quality.