Senior Researcher (Explainable Reasoning)

microTECH Global LTD; Helsinki; On-site; EUR 85,000 - 125,000; Full time

Job summary

microTECH Global LTD in Helsinki is seeking a passionate researcher to push frontiers at the intersection of AI and safety, translating breakthroughs into production-ready tools. The role focuses on design architectures for multimodal AI systems and building benchmarks for reliability and alignment.

Required are a PhD and a proven track record in Explainable AI, safety, and neural-symbolic methods, with strong coding skills in Python/C++ and experience with PyTorch/TensorFlow.

Qualifications

• PhD in a relevant field with strong research track record.
• Proven expertise in Explainable AI and safety in AI systems.
• Strong command of LLMs, neural nets and computer vision.

Responsibilities

• Design and implement architecture for AI detection system with multimodal data.
• Explore novel techniques for visual question answering, reasoning and grounding.
• Develop interpretable, unbiased, aligned AI systems for real-world use.

Skills

LLMs

Neural networks

Computer vision

Neuro-symbolic

Python

Java

C++

PyTorch

TensorFlow

Explainable AI

AI safety

Education

PhD in Computer Science, Deep Learning, ML or related field

Tools

Knowledge graphs

Job description

We are looking for a passionate and motivated researcher with a solid track record of solving challenging problems, advancing state-of-the-art, and demonstrated passion for ground-breaking research that could be scaled to production environment with an emphasis on the intersection of AI and Safety.

Your responsibilities:

• Design and implement effective, efficient and novel architecture to enable AI detection system that understands and able to reason over complex multimodal data, such as images, videos, text and cultural knowledge.
• Explore novel techniques for visual question answering, visual reasoning, text-based reasoning, and multimodal knowledge grounding. Explore the use of knowledge graphs and other neural-symbolic techniques to enhance reasoning capabilities.
• Create new techniques to make AI models/Agentic AI system more interpretable, unbiased, transparent and aligned with human values for real-world scenarios.
• Create new techniques to ensure consistent alignment between user-centric explanations and intrinsic model behavior. Develop and implement state-of-the-art alignment techniques (e.g., RLHF, RLAIF, Constitutional AI) specifically tailored for LLMs CoT, agentic workflows and multi-step reasoning.
• Reverse-engineer on neural networks to understand internal workings of AI models rather than treating them as "black boxes". Implement and integrate AI explainbility techniques and tools.
• Develop data attribution methods to quantify the influence of specific data points (text and image) on a model's prediction and implement solution.
• Build rigorous benchmarks and datasets to measure the quality of reasoning capabilities and reliability, quality of user-centric explanations, model transparency, and effectiveness of treatments.
• Develop robust defenses against jailbreaking, prompt injections, and adversarial exploits that target a model’s planning and tool-use capabilities.
• Communicate insights to stakeholders.
• Turn cutting-edge AI detections and AI safety papers into high-performance, scalable code, transforming theoretical breakthroughs into production-ready tools and frameworks.

Requirements:

• Ph.D. in Computer Science, Deep Learning, Machine Learning, Mathematics or other related fields.
• Focused on research in the AI field with good track records and high motivation in Explainable AI and Safety domain.
• Strong proficiency in Large Language Models (LLMs), neural networks, and computer vision, neuro-symbolic architectures.
• Strong background in Python, Java, or C++, with deep knowledge of ML frameworks such as PyTorch, TensorFlow.
• Successful experience in AI alignment to human values and expectations, model robustness improvement, controlled and continual learning, neural network interpretability and editing techniques is highly valued.
• Pioneering novel methods and neural networks that revolutionized machine learning or the AI field, or revolutionized the industry, is a big bonus.