Overview As Lead Applied Scientist at TR Labs, you define research strategy and steer high-impact AI initiatives across product features. You will advance domains like legal reasoning, document understanding, and knowledge graphs, driving best practices and technical vision. You mentor scientists, engage stakeholders, and translate customer problems into real AI capabilities. This role offers the chance to shape AI roadmaps and deliver real-world impact in a global information services leader.
Compensation / Benefits- hybrid work model
- flexible work-life policies (Flex My Way)
- mental health days and Headspace access
- tuition reimbursement
- retirement savings
- employee incentive programs
Responsibilities- Define research strategy and lead high-impact AI initiatives across product features
- Drive innovation in domains such as legal reasoning, document understanding, and knowledge graphs
- Establish best practices, technical vision, and evaluation methodologies
- Mentor scientists across labs and lead stakeholder engagement
- Develop deep understanding of customer problems and the data that informs them
- Translate complex customer problems into successful AI applications and products
Key requirements- PhD or Master's in Computer Science or related field
- 7+ years hands-on experience building production IR, NLP, ML, or GenAI systems
- Experience leading cross-functional teams to deliver end-to-end AI capabilities
- Strong product mindset and ability to translate customer problems into AI solutions
- Proven ability to introduce novel methods influencing product roadmaps and business outcomes
- Excellent communication and analytical skills
- Deep expertise in RAG architectures, agentic frameworks, tool-using systems, and evaluation methodologies
- Significant experience developing novel methods for professional domains (medical, tax, or law)
- Publications at top-tier venues (ACL, EMNLP, NAACL, NeurIPS, ICLR, SIGIR, KDD)
- Experience delivering production code with solid software/MLOps practices
- communication
- analytical thinking
- mentorship
- RAG architectures
- agentic frameworks
- tool-using systems
