As the Lead Applied Scientist, you will guide a team focused on building real-world AI systems that solve complex challenges using natural language processing, information retrieval, and generative artificial intelligence. You'll lead end-to-end development—from initial concept and proof-of-concept to scalable deployment and iterative improvement based on user feedback.
Key Responsibilities
- Direct applied research initiatives across the full product lifecycle, ensuring scientific rigor and technical excellence.
- Collaborate closely with product managers, engineers, and subject matter experts to align AI innovation with customer needs and business goals.
- Translate deep understanding of customer problems into effective AI-driven solutions, leveraging both structured and unstructured text data.
- Mentor scientists and engineers in best practices for model development, experimentation, and deployment, fostering a culture of learning and technical excellence.
- Stay current with advancements in transformer architectures, large language models, and agentic frameworks to ensure state-of-the-art approaches are applied where valuable.
- Contribute strategic insights on AI direction to senior leadership, helping shape the future of intelligent systems within the organization.
Qualifications
Candidates must hold a PhD in a relevant field or a master’s with equivalent experience, along with at least seven years of hands-on work developing NLP and IR systems for commercial use. You should have a proven ability to lead technical teams in applied research settings and deliver production-grade code using Python, Git, and cloud platforms such as AWS or Azure.
Required expertise includes transformer-based models, prompt engineering, retrieval-augmented generation (RAG), fine-tuning language models, and implementing agentic workflows using frameworks like LangGraph or AutoGen. Experience designing auto-evaluation methods and optimizing datasets for ML validation is essential. Familiarity with agile development, lightweight UI prototyping, and rapid iteration is expected.
Preferred qualifications include background in legal technology, experience with long-form document analysis, or publications in top-tier AI conferences such as ACL, NeurIPS, or KDD. Knowledge of MLOps and the full AI lifecycle is a strong plus.
Work Environment & Culture
This role operates in a hybrid model, with team locations in the United States and Canada. Employees enjoy flexible arrangements, including the ability to work remotely for up to eight weeks per year. The organization emphasizes customer focus, fast learning, inclusive collaboration, and social impact—supporting initiatives that advance justice, transparency, and the rule of law.
Benefits include comprehensive health coverage, retirement planning with company match, generous paid time off, mental health days, parental and sabbatical leave, tuition reimbursement, and wellness resources. Career growth is supported through skills-first development programs and opportunities for pro-bono project involvement.
