Articul8 AI is looking for a Senior/Staff AI Researcher to conduct advanced research that pushes the capabilities of generative AI systems. You will design, implement, and evaluate novel approaches, working directly at the intersection of research and product development to create exceptional AI products.
What You'll Do
- Serve as a subject matter expert in GenAI domains, including data pipelines, training methodologies, reinforcement learning, multimodal AI, personalization, and knowledge representation.
- Play a technical leadership role in designing, developing, and scaling novel algorithms and models from research prototypes to production-ready systems.
- Partner with cross-functional teams to integrate research findings into products.
- Monitor and analyze emerging trends in generative AI, sharing contributions through publications.
What We're Looking For
- A PhD or MSc degree in Computer Science, Machine Learning, or a related field.
- 5+ years of experience as an AI researcher with a track record of applied research and/or product development.
- At least 2+ years actively developing GenAI technologies.
- Experience developing tools, libraries, and infrastructure for data preprocessing, model training/finetuning, and deployment of LLMs in research and production.
- Strong background in parallel and distributed computing on the cloud.
- Background in machine learning, deep learning, probability theory, statistics, NLP, computer vision, data wrangling, and model evaluation.
- Proficiency in Python and experience with version control systems like Git for collaborative development.
Nice to Have
- Experience with cloud computing platforms such as AWS, Azure, or GCP.
- A proven track record of publications in top-tier conferences and journals.
Technical Stack
- Python
- Git
- AWS, Azure, GCP
Team & Environment
You will be a member of the Applied Research team, working alongside dedicated individuals who take pride in their work and relentlessly pursue excellence.
Work Mode
This position is based in Brazil and follows a local-country work mode.




