Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A rapidly growing AI company is developing an advanced contract intelligence platform designed to transform how organizations review, understand, and manage agreements. The platform is used by major global companies and applies artificial intelligence to complex legal workflows where accuracy, explainability, and reliability are critical.
The engineering organization is focused on developing innovative AI capabilities across contract intelligence, including AI agents, retrieval-augmented generation, large-scale legal fact extraction, contract database search, document clustering, risk detection, and the creation of composite contracts from amendments and related documents.
The organization operates with an inventor mindset, bringing advanced AI research directly into production and continuously exploring new approaches to complex problems in legal technology.
What You’ll Do
- Own a research roadmap from identifying high-impact problems through experimentation, prototyping, evaluation, and production deployment.
- Advance the core AI platform by developing novel approaches to long-context reasoning over legal documents, contract comparison and redlining, information extraction, automated drafting, and document editing.
- Improve model trustworthiness through research into procedural hallucination detection and resolution, calibration, explainability, and grounded generation.
- Develop techniques that improve the accuracy, reliability, and transparency of AI systems operating in high-stakes legal environments.
- Explore advanced fine-tuning, parameter-efficient fine-tuning (PEFT), and model distillation techniques to improve model performance, speed, and cost efficiency.
- Evaluate emerging research in agentic systems, long-context modeling, and reasoning to determine which approaches deliver meaningful improvements for customers.
- Build and maintain datasets, benchmarks, and evaluation systems for measuring model performance on complex legal text.
- Define meaningful performance metrics and establish rigorous standards for AI model evaluation.
- Partner closely with Engineering and Product teams to move successful research prototypes from experimentation into production.
- Prepare internal research reports that influence the technical direction of the AI platform.
- Present research findings and technical insights to both technical and non-technical audiences across the organization.
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.
- Evidence of exceptional technical or research ability demonstrated through publications, shipped systems, open-source contributions, competition results, or solving complex technical problems.
- 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, reasoning, parameter-efficient fine-tuning (PEFT), quantization, inference optimization, speculative decoding, hallucination mitigation, novel deep learning architectures, or robust LLM evaluation methodologies.
- Excellent communication skills with the ability to explain complex research findings clearly to both technical and non-technical audiences.
- Strong bias toward action and experimentation, with an emphasis on shipping working prototypes, measuring results, and iterating based on evidence.
Nice to Have
- Publications at leading ML/AI conferences such as NeurIPS, AAAI, ICML, or ICLR, or peer-reviewed publications in related quantitative fields.
- Equivalent high-impact research contributions developed in an industry environment.
- Experience with long-context modeling, retrieval, or grounded generation in high-stakes industries such as legal, medical, or financial services.
- Prior research experience in hallucination detection, model calibration, or interpretability.
- Demonstrated ability to build new systems and research capabilities from the ground up in fast-paced startup or research environments.
Disclaimer
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