Job Description
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Company Overview
A technology company is developing AI-powered products designed to help businesses address complex contracting challenges. Its contract intelligence platform applies artificial intelligence to legal workflows, helping organizations analyze, review, compare, search, and manage large volumes of contractual information.
The organization has developed AI capabilities across document editing, agentic retrieval-augmented generation, large-scale legal fact extraction, contract database search, document clustering, deviation analysis, and contract amendment processing.
The organization is seeking a Senior AI Researcher to advance the application of large language models, deep learning, and advanced AI techniques to complex legal-domain challenges. The role focuses on developing highly accurate, explainable, and robust AI systems where reliability and grounded outputs are essential.
Key Responsibilities
- Advance the core AI platform by designing and implementing novel approaches for reasoning over long-context legal documents, contract comparison and redlining, information extraction, automated drafting, and document editing.
- Research methods for hallucination detection and resolution, model calibration, explainability, and grounded generation.
- Explore and apply advanced fine-tuning, parameter-efficient fine-tuning (PEFT), and model distillation techniques to improve model accuracy, efficiency, and cost.
- Evaluate emerging techniques in agentic systems, long-context modeling, and AI reasoning and determine their practical value for customer-facing applications.
- Design and maintain datasets, benchmarks, and evaluation systems for training and measuring model performance on complex legal text.
- Define meaningful evaluation metrics and establish standards for measuring AI system performance.
- Partner with Engineering and Product teams to move research prototypes from experimentation into production.
- Produce internal research reports that help shape the technical direction of the AI platform.
- Present research findings and technical insights to both technical and non-technical audiences.
- Own research initiatives end-to-end, including identifying problems, designing experiments, developing prototypes, evaluating results, and supporting production deployment.
Required Qualifications
- Ph.D. in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field, or equivalent industry research experience with a comparable track record.
- Demonstrated exceptional technical or research ability through publications, shipped systems, open-source contributions, competition results, or other significant technical accomplishments.
- Deep hands-on experience in deep learning research and development, particularly involving large language models.
- Strong working knowledge of modern machine learning frameworks such as PyTorch, JAX, or TensorFlow and the surrounding open-source ecosystem.
- Expertise in at least one of the following areas:
- Agentic systems
- AI reasoning
- Parameter-efficient fine-tuning (PEFT)
- Quantization
- Inference optimization, including techniques such as speculative decoding
- Hallucination mitigation
- Novel deep learning architectures
- Robust evaluation methodologies for large language models
- Excellent communication skills with the ability to clearly explain complex research findings to technical and non-technical audiences.
- Strong execution skills with an emphasis on experimentation, measurement, rapid prototyping, and production delivery.
Preferred Qualifications
- Publications at leading ML/AI conferences such as NeurIPS, AAAI, ICML, or ICLR.
- Peer-reviewed publications in quantitative fields or related journals such as the Journal of Computational Physics, SIAM journals, Journal of the ACM, Nature, Science, or PNAS.
- Equivalent significant research contributions developed in an industry environment.
- Experience with long-context modeling, retrieval, or grounded generation in high-stakes domains such as legal, medical, or financial applications.
- Previous work involving hallucination detection, model calibration, or AI interpretability.
- Experience building products, research systems, or technical capabilities from the ground up in fast-paced startup or research environments.
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