[Up to c. $300k Comp Package | Office-Led Working]
Role Overview
We’re representing a global multi-strategy investment firm building out a dedicated AI Labs capability in New York. This is a research-engineering role for someone who sits comfortably between applied AI experimentation and production-grade software delivery. You will explore emerging AI techniques, test what works, and turn the strongest ideas into secure, observable internal tools and reusable services for teams across the firm.
The remit spans LLMs, agents, retrieval, evaluation, knowledge systems, model optimisation and workflow automation. The successful hire will not just prototype new ideas - they will help define how AI is engineered, measured and deployed in a high-performance investment environment...
Key Responsibilities
- Explore new applied AI methods across language models, agent-based systems, retrieval, structured knowledge, simulation, workflow automation and compact model architectures
- Build early-stage proofs of concept, then develop the strongest ideas into reusable internal tooling, services and engineering patterns
- Create robust testing and measurement frameworks to assess model behaviour, retrieval quality, agent performance, prompt design and workflow reliability
- Improve AI system performance through better context design, model selection, prompt strategy, feedback mechanisms, tuning approaches and output-quality controls
- Develop core AI platform components across model access, routing logic, retrieval layers, context preparation, orchestration and automated evaluation
- Work closely with engineering, data, investment and business teams to move AI Labs work into secure, monitored and maintainable production environments
- Help define internal best practice for AI engineering through shared code, technical documentation, reusable libraries and practical guidance for other teams
What You’ll Bring…
- 3-6 years’ experience building ML, AI or data-driven software systems, or 3+ years’ relevant experience alongside a PhD in a related technical field
- Experience moving AI, ML, agent-based or model-driven systems from prototype into production
- Strong Python engineering skills across services, pipelines and modern cloud, ML, data or AI platforms
- Hands-on applied AI experience across prompting, retrieval, embeddings, agents, evals, tuning or model-assisted workflows
- Depth in LLMs, agents, semantic search, information retrieval, evals, knowledge graphs, structured reasoning, model compression or multi-agent systems
- Strong experimental judgement across benchmarks, comparisons, result interpretation and evidence-led recommendations
- Clear understanding of trade-offs across quality, latency, cost, scalability, reliability and user experience
- Ability to turn new technical research into clean, tested and maintainable engineering solutions
- Strong stakeholder skills across investment, data, technology and business teams
- (Preferred) Experience in investment management, trading, financial services, big tech, AI labs or ML platform teams
- (Preferred) Exposure to production RAG, AI agents, vector search, model evaluation, orchestration, AI observability or model serving
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