Responsibilities
- Investigate, architect, construct, and evaluate low-level software systems, including operating environments, compilers, and network-distributed applications tailored for extensive social data and predictive modeling.
- Contribute deep professional expertise in areas such as ranking systems, classification frameworks, recommendation engines, and optimization challenges—including fraud detection in payments, click-through or conversion rate forecasting, ad or feed ranking, sentiment analysis, collaborative filtering, and spam identification—or possess advanced knowledge in contemporary generative models and foundational architectures like large language models, transformers, and diffusion networks.
- Address complex, large-scale challenges by creating highly scalable algorithms, systems, and tooling using deep learning, regression techniques, and rule-based methodologies.
- Propose, gather, assess, and integrate system requirements while diagnosing constraints across technologies, platforms, and development tools.
- Develop agile, data-intensive solutions that rapidly iterate and apply cutting-edge deep learning methods to leverage vast data volumes.
- Exhibit strong software engineering discipline, work independently with limited oversight, and guide less experienced team members through mentorship.
- Utilize advanced machine learning techniques effectively within modern parallel computing environments, including distributed clusters and GPU-accelerated systems.
- Lead small teams or technical initiatives when required, offering architectural insight, conducting code reviews, and providing technical direction.
Work Arrangement
On-site
