Lead Specialist, AI Scientist

This is a fully remote position, open to applicants in Poland.

📋 Description

• Oversee AI modeling initiatives from inception to completion, ensuring the solution is comprehensible, adoptable, and maintainable by partner teams.

• Provide guidance to product, data, and engineering teams regarding AI solution architecture, including model selection, data needs, architectural choices, and the associated trade-offs.

• Act as a technical resource within the C4E and its partner teams: evaluate methodologies, address challenging inquiries, and offer informed second opinions on critical decisions.

• Modify model designs and methodologies according to feedback from partners and validation outcomes, maintaining a balance between technical precision and practical limitations.

• Recognize opportunities for enhancing the team's modeling practices and take action: improve evaluation techniques, develop shared templates, and clarify processes.

• Develop reusable technical assets such as design patterns, evaluation frameworks, and model documentation, while actively promoting knowledge sharing across different disciplines.

• Collaborate with the Responsible AI, Data, Platform, and Security teams to ensure appropriate involvement at each stage and relay recurring themes or gaps back to them.

• Assist partners in adoption by generating usable documentation and transition materials, remaining engaged until teams feel confident with the developed solutions.

⛳️ Requirements

• Extensive hands-on experience in building and deploying ML or deep learning models, including complex projects with actual production demands.

• Proficient in Python and knowledgeable across the ML stack (e.g., PyTorch, Hugging Face), with a strong grasp of experimental design and rigorous evaluation practices.

• Proven capability to provide recommendations on AI solution design and clearly communicate trade-offs to both technical and non-technical stakeholders, including senior leadership.

• History of adapting technical strategies based on feedback and new findings.

• Experience collaborating across disciplines (data, product, research, compliance) on AI projects of significant scope and complexity.

• Excellent technical writing abilities and strong facilitation skills.

• Experience with generative AI, including LLM fine-tuning, RAG architectures, prompt engineering, or assessment of LLM-based systems. (Nice to Have)

• Familiarity with educational technology, assessment, speech processing, or language learning sectors. (Nice to Have)

• Significant exposure to responsible AI practices: addressing fairness, bias, or explainability challenges in real-world projects, not merely in theory. (Nice to Have)

• Experience in enhancing the operational efficiency of a data science or ML team, rather than just individual contributions. (Nice to Have)

• Familiarity with MLOps tools and cross-team AI governance processes. (Nice to Have)

• Previous experience in an advisory or enablement capacity. (Nice to Have)

🏝️ Benefits

• N/A