Lead Research Engineer
Description du poste
Overview
In this role you lead and shape AI research initiatives at Thomson Reuters Labs. You drive methodology, evolve the tech stack, and mentor teams to deliver scalable ML solutions. You work across a global, interdisciplinary group to transform how professionals use data-driven tools. You thrive in a fast, collaborative environment and own end-to-end delivery of innovative research-powered software. This is an opportunity to impact real-world domains like legal, tax, accounting, and government with cutting-edge AI.
Leistungen / Benefits- Hybrid Work Model
- flexible work arrangements including work-from-anywhere
- Career Development and Growth programs
- industry competitive benefits package
- Mental Health days and well-being resources
- volunteer and ESG initiatives
- Provide technical leadership and establish scalable standards and practices across initiatives
- Own the full software development lifecycle to build, test, and deliver high-quality solutions
- Create large-scale data processing pipelines and production-ready ML systems
- Collaborate with cross-functional and remote teams to share knowledge and align on goals
- Work in an agile, fast-paced environment to deliver timely solutions
- Contribute innovative ideas and be accountable for end-to-end deliveries
- Communicate effectively with stakeholders and translate concepts between research and engineering
- Bachelor of Science in Computer Science or related field; 8+ years in software engineering, with ML/NLP context
- Experience leading technical workstreams within a software organization
- Strong Python expertise; familiarity with other languages (Java, TypeScript, JavaScript)
- Hands-on with Python data science stack (NumPy, SciPy, Pandas, Dask, spaCy, NLTK, scikit-learn, PyTorch)
- Production ML experience, including ModelOps/MLOps concepts and cloud familiarity (AWS or Azure)
- Proven mentoring ability and experience elevating team technical practices
- Exposure to NLP tasks (NER, information extraction, retrieval) and research-to-production translation
- Automation, system monitoring, and cloud-native application proficiency
- collaborative and cross-functional mindset
- strong communication and stakeholder engagement
- mentorship and team development
- Python programming
- NLP libraries: spaCy, NLTK
- ML frameworks: PyTorch, scikit-learn
