The Algo Data Science team develops and integrates sophisticated AI systems and advanced data frameworks to improve data usage in training for several features. We are using the most advanced technologies including Large Language Models, Foundation Models and intelligent agents into our technologies and workflows.
We are looking for an outstanding student to help improve our data infrastructure for efficient and advanced training for the Depth Sensing group. If you are excited about the intersection of LLMs, intelligent systems, and data, this role will challenge you.
In this role, you will join a team of researchers and engineers developing smart solutions for data handling during training, validation and evaluation to improve the performance of models and complicated systems for cutting-edge our products.
You will contribute to the entire system lifecycle from data retrieval to automation of pipelines and models success for depth and 3D applications.
Responsibilities:
Develop scalable frameworks for efficient data usage and processing pipelines.
Create innovative tools to analyze and enhance model performance.
Build and maintain automated data pipelines for training, validation, and evaluation workflows.
Implement version control best practices and contribute to collaborative codebases.
Collaborate cross-functionally with researchers and engineers from multiple teams.
Research and develop frameworks for advanced filtering and anomaly detection.
Optimize data storage and retrieval systems for large-scale ML workflows.
Requirements: Minimum Qualifications:
Student pursuing Master's degree in Computer Science, Electrical Engineering, Software Engineering, or related field with ML focus.
Independent, self-motivated with strong creativity and innovation skills.
Strong analytical thinking and problem-solving abilities.
Strong coding skills.
Experience with Python and machine learning frameworks.
Familiarity with version control systems (Git) and collaborative development workflows.
Understanding of software development best practices and code review processes.
Preferred Qualifications:
Experience with cloud platforms (AWS) and distributed computing.
Familiarity with data storage solutions (AWS S3, data lakes, distributed file systems).
Experience with containerization (Docker) and orchestration tools.
Knowledge of Foundation Models and/or Large Language Models.
Familiarity with LLM application frameworks (Hugging Face, OpenAI APIs).
Experience with data pipelines.
Understanding of ML ops and deployment practices.
This position is open to all candidates.