Job Description
Title: Applied AI Researcher
Location: Jersey City, NJ(Hybrid)
Type: Contract
Description:
- Bridge advanced AI research and practical enterprise use cases by validating models, methods, and prototypes that can become production-grade solutions. The role focuses on measurable business value, rigorous experimentation, model behavior, and safe translation of research into banking-relevant applications.
- Primary ownership
- Applied research agenda for LLMs, NLP, RAG, evaluation, multimodal AI, and agentic workflows.
- Prototypes, experiments, benchmark design, and model selection recommendations.
- Research-to-production handoff with AI engineering and platform teams.
- Key responsibilities
- Conduct applied research in LLMs, GenAI, NLP, information retrieval, multimodal AI, synthetic data, and agentic AI.
- Design experiments to evaluate model performance, robustness, safety, scalability, interpretability, and enterprise usefulness.
- Prototype AI solutions for use cases such as document intelligence, financial analysis, compliance support, knowledge retrieval, and operational automation.
- Develop evaluation methodologies using golden datasets, adversarial testing, offline benchmarks, human review, and business outcome metrics.
- Assess prompt optimization, RAG, fine-tuning, instruction tuning, synthetic data generation, distillation, and model adaptation techniques.
- Collaborate with engineers to convert prototypes into production-ready systems with clear requirements, limitations, and acceptance criteria.
- Track emerging AI research and translate relevant advances into practical recommendations for the enterprise.
- Produce internal research papers, technical notes, implementation guides, and thought-leadership materials.
Must-have candidate profile:
- Advanced degree preferred, usually MS or PhD in AI, ML, computer science, statistics, computational linguistics, mathematics, or related field.
- Strong foundation in machine learning, deep learning, NLP, transformers, information retrieval, and generative AI.
- Hands-on experience with LLMs, embeddings, RAG, model evaluation, and applied GenAI experimentation.
- Python skills with PyTorch, TensorFlow, Hugging Face, scikit-learn, or equivalent research frameworks.
- Ability to design rigorous experiments and communicate findings to technical and business stakeholders.
- Preferred experience
- Research or applied science experience in banking, finance, compliance, risk, legal, operations, or enterprise knowledge systems.
- Publications, patents, internal research contributions, or open-source AI contributions.
- Familiarity with Responsible AI, model validation, privacy constraints, and regulated deployment environments.
- Initial screening questions
- What research idea did you convert into a prototype or production capability?
- How do you design an evaluation harness for an LLM-based banking use case?
- How do you determine whether fine-tuning, RAG, or prompting is the right approach?
- What failure modes did you discover and how did you mitigate them?
- How do you communicate model limitations to non-research stakeholders?
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