AI Platform Developer

Qube Research & Technologies•New York, NY

5d•$180,000 - $300,000

About The Position

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT's collaborative mindset which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors. You'll help build and operate QRT's internal AI platform, enabling researchers, developers and data scientists to leverage LLM-powered tools effectively at scale. Working alongside experienced Platform Engineers, you'll contribute to production AI services including RAG pipelines, agentic workflows, retrieval infrastructure and internal APIs. This role is ideal for an early-career engineer or recent graduate with a strong foundation in AI and machine learning who is excited to bring cutting-edge models into production systems.

Requirements

Bachelor's, Master's or PhD in Computer Science, Machine Learning, Engineering, Mathematics or another quantitative field, or equivalent practical experience.
Strong foundation in AI and machine learning, including exposure to transformers and reinforcement learning.
Strong Python skills and an interest in building production-grade software.
Experience with PyTorch or another deep learning framework.
Understanding of LLM fundamentals, including prompting, context management and model limitations.
Experience building AI/ML applications or working with LLMs or other deep learning models.
Exposure to cloud platforms (e.g. AWS) and modern software engineering practices.
Strong communication skills and the ability to collaborate across technical and non-technical teams.

Nice To Haves

Experience deploying ML or LLM models into production.
Familiarity with RAG systems, vector databases and retrieval techniques.
Experience with agentic AI systems, workflow orchestration or LLM evaluation.
Understanding of embeddings, retrieval performance and human-in-the-loop systems.
Experience building APIs, data pipelines or observability tooling.
Contributions to open-source projects, research publications or ML competitions (e.g. Kaggle).

Responsibilities

Build and enhance internal AI services, APIs and production applications.
Contribute to RAG pipelines, including document ingestion, embeddings and retrieval.
Support the deployment of transformer-based models and agentic workflows.
Design scalable, well-documented APIs for internal users.
Improve service reliability, latency and evaluation frameworks.
Contribute to prompt management, testing and human-in-the-loop controls.
Build resilient AI systems with monitoring and fallback mechanisms.
Implement monitoring, tracing and quality metrics across AI services.
Support deployment, versioning and lifecycle management.
Participate in operational support and incident response alongside senior engineers.