Senior AI Research Engineer
Quantum is building an AI-driven trading platform that generates, tests, and deploys sophisticated trading strategies across digital and traditional markets.
We are looking for a hands-on Senior AI Research Engineer to help design and evolve the AI systems powering the platform. The role sits at the intersection of applied AI research and software engineering, taking ideas from research through to reliable production systems.
This is not a traditional data science role. You will work across the full stack and should enjoy building AI-native systems in fast-moving, early-stage environments.
Key Responsibilities• Design, build, and ship LLM-driven and multi-agent systems for strategy generation, market intelligence, and quantitative research.
• Develop evaluation, experimentation, and monitoring frameworks for models and agents in production.
• Contribute to AI infrastructure, inference serving, and data pipelines across the platform.
• Research and evaluate emerging AI techniques, models, and architectures relevant to the platform.
• Improve model robustness, inference performance, and operational reliability.
Key Skills and Experience• 5+ years in data science or ML engineering, with recent applied AI / LLM experience.
• Strong Python and modern AI stack: PyTorch, Hugging Face, LLM serving (vLLM, SGLang), agent frameworks (OpenAI Agents SDK, LangGraph), and provider APIs (OpenAI, Anthropic).
• Track record building and deploying production AI/ML systems, with strong evaluation harness design (agent trajectories, LLM-as-a-judge, trace and error analysis).
• Hands-on with open-source LLM fine-tuning (SFT, LoRA, QLoRA, DPO, GRPO) and libraries such as TRL, Axolotl, or Unsloth.
• Comfortable across the software stack: Docker, Kubernetes, Azure, microservices.
• Numerate background, ideally an MSc in a quantitative subject (Maths, Physics, Computer Science, Engineering).
Preferred• Shipped LLMs or agentic workflows into production.
• Experience with financial datasets or quantitative research environments.
• Familiarity with LLM observability and experiment tracking (Weights & Biases, LangSmith, Langfuse, MLflow).
- Familiarity with RAG and vector databases (pgvector, Pinecone, Weaviate).