At a Glance
- Tasks: Join us as a Computational Linguist to develop and maintain our Knowledge Graph infrastructure.
- Company: Be part of a dynamic team focused on language, data, and semantic technologies.
- Benefits: Enjoy a fully remote role with a competitive rate of Β£50-60 per hour.
- Why this job: This role offers a chance to impact knowledge management while collaborating with diverse teams.
- Qualifications: Experience in taxonomy, ontology development, and familiarity with NLP and LLMs is essential.
- Other info: Contract duration is 6 months, with potential for extension.
We are looking for a skilled and motivated Computational Linguist to join our team and take ownership of the development and maintenance of our Knowledge Graph infrastructure. This role is ideal for someone passionate about languages, structured data and semantic technologies, with a strong background in taxonomies, ontologies and knowledge management. You will play a critical role in ensuring the coherence, scalability and long-term sustainability of our knowledge base systems. A solid understanding of machine learning and prompting techniques is highly desirable, along with the ability to critically assess how ML-driven solutions can be implemented effectively β including their potential benefits, limitations, and associated risks β in the context of knowledge representation and management. The ideal candidate will have experience working at the intersection between linguistics and programming, with the ability to define and maintain consistent criteria across a growing graph-based architecture. This position requires excellent analytical thinking, organizational autonomy, and the ability to collaborate effectively with both technical and non-technical profiles and teams.
Key Responsibilities
- Design, develop and maintain the structure and content of the Knowledge Graph.
- Maintain high-quality standards in manual labeling processes to ensure taxonomies population adheres to established guidelines.
- Define and update taxonomies, ontologies and classification systems to ensure semantic consistency and scalability across Redditβs diverse content landscape.
- Collaborate with engineers, domain experts and curators/labellers teams to align taxonomy designs with Reddit goals.
- Organize and prioritize tasks both independently and coordinate with teammates for aligned deliverables.
- Document processes and contribute to the continuous improvement of workflows, tools and quality standards.
Hard Skills
- Proven experience in taxonomy and ontology development and management.
- Strong understanding of linguistics annotation, language resources and semantic metadata.
- Detail-oriented mindset, with a proactive approach to error detection and quality assurance.
- Familiarity with LLMs and NLP pipelines (entity linking, topic modeling, text classification, sentiment analysis, etc).
- Familiarity with data querying and manipulation (SQL, Python, etc).
Must Have
- Data Annotation
- Data querying and manipulation
- Familiarity with LLMs
- Natural Language Processing (NLP)
- Ontology Development
- Taxonomy Management
Soft Skills
- Strong organizational skills and ability to manage both individual and shared responsibilities with autonomy.
- Excellent communication and documentation abilities to cross-functional collaboration.
- Team player with a collaborative spirit and the ability to coordinate across multidisciplinary teams.
- Problem-solving attitude and willingness to iterate and refine existing processes.
Computational Linguist employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice π€«
We think this is how you could land Computational Linguist
β¨Tip Number 1
Familiarise yourself with the latest trends in computational linguistics and knowledge graph technologies. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
β¨Tip Number 2
Network with professionals in the field of computational linguistics and knowledge management. Attend relevant webinars, workshops, or meetups to connect with potential colleagues and learn about their experiences.
β¨Tip Number 3
Prepare to discuss specific projects where you've applied your skills in taxonomy and ontology development. Be ready to explain your thought process and the impact of your work on previous teams or projects.
β¨Tip Number 4
Showcase your problem-solving skills by thinking through potential challenges that might arise in the role. Consider how you would approach these issues and be prepared to share your ideas during the interview.
We think you need these skills to ace Computational Linguist
Some tips for your application π«‘
Tailor Your CV: Make sure your CV highlights relevant experience in computational linguistics, taxonomy and ontology development. Use specific examples that demonstrate your skills in managing knowledge graphs and working with semantic technologies.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for languages and structured data. Explain how your background aligns with the role's requirements, particularly your experience with machine learning and NLP pipelines.
Highlight Relevant Skills: In your application, emphasise your hard skills such as data querying, manipulation, and familiarity with LLMs. Also, mention your soft skills like organisational abilities and teamwork, which are crucial for this role.
Proofread Your Application: Before submitting, carefully proofread your CV and cover letter to eliminate any errors. A well-presented application reflects your attention to detail, which is essential for a role focused on quality assurance.
How to prepare for a job interview at Russell Tobin
β¨Showcase Your Technical Skills
Be prepared to discuss your experience with taxonomy and ontology development. Highlight specific projects where you've successfully implemented these skills, especially in relation to knowledge management and semantic technologies.
β¨Demonstrate Analytical Thinking
Expect questions that assess your analytical abilities. Prepare examples of how you've approached complex problems in the past, particularly those involving machine learning and its application in linguistics.
β¨Emphasise Collaboration
Since the role involves working with both technical and non-technical teams, be ready to share experiences where you've effectively collaborated across disciplines. Highlight your communication skills and how you ensure alignment on project goals.
β¨Prepare for Scenario-Based Questions
Think about potential scenarios related to knowledge graph maintenance and data quality assurance. Be ready to discuss how you would handle challenges such as ensuring semantic consistency or managing taxonomies across diverse content.