Lead Research Engineer

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** This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization.

We experiment, we build, we deliver. We obsess over our customers through applied research and development of new products and technologies. In TR Labs, we act fast and learn fast, innovating collaboratively across our core segments in Legal, Tax & Accounting, Government, and Reuters News.

As a Lead Research Engineer at Thomson Reuters Labs, you will be part of a global interdisciplinary team of experts. We hire engineers and specialists across a variety of AI research areas to drive the company’s digital transformation. The science and engineering of AI are rapidly evolving. We are looking for a lead who drives strategy across initiatives, shapes engineering practices and standards, and who force multiplies the capabilities of our teams through mentorship and technical leadership.
** About the Role
** In this opportunity as a Lead Research Engineer, you will:
* Be a Leader:
Provide technical leadership partnering with other engineers to develop and improve methodology and evolve the technology stack by establishing standards and best practices that scale.
* Develop and Deliver:
Applying modern software development practices, you will be involved in the entire software development lifecycle, building, testing and delivering high-quality solutions.
* Build Scalable ML Solutions:
You will create large scale data processing pipelines to help researchers build and train novel machine learning algorithms.  You will develop high performing scalable systems in the context of large online delivery environments.
* Be a Team Player:
Working in a collaborative team-oriented environment, you will share information, value diverse ideas, partner with cross-functional and remote teams.
* Be an Agile Person:  With a strong sense of urgency and a desire to work in a fast-paced, dynamic environment, you will deliver timely solutions.
* Be Innovative:
You are empowered to try new approaches and learn new technologies. You will contribute innovative ideas, create solutions, and be accountable for end-to-end deliveries.
* Be an Effective Communicator:
Through dynamic engagement and communication with cross-functional partners and team members, you will effectively articulate ideas and collaborate on technical developments.
** About You
*** A Bachelor of Science degree, computer science or related field
* At least 8 years software engineering experience, ideally in the context of machine learning and natural language processing
* Experience leading technical work streams within a software engineering organization.
* Skilled and have a deep understanding of Python software development stacks and ecosystems, experience with other programming languages and ecosystems is ideal.
* Driving innovation throughout the entire software lifecycle – specify, design, build, scale and maintain Machine Learning systems and capabilities in production environments.
* Familiarity with the Python data science stack through exposure to libraries such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, scikit-learn, PyTorch.
* Take pride in writing clean, reusable, maintainable and well-tested code
* Experience in collaborating with research scientists to evaluate, prototype and product ionize research concepts
* Proficiency in automation, system monitoring, and cloud-native applications, with familiarity in AWS or Azure (or a related cloud platform)
* Proficiency in system analysis and design & Consider Dev Ops and automation as fundamental pillars of your work
* A desire to learn and embrace new and emerging technology
* Familiarity with probabilistic models and have an understanding of the mathematical concepts underlying machine learning methods
* Demonstrated ability to mentor engineers, elevate team technical practise
* Experience integrating Machine Learning solutions into production-grade software with a sound understanding of Model Ops and MLOps principles and the ability to translate between language and methodologies used both in research and engineering fields
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