Senior Researcher (Explainable Reasoning) - Finland

microTECH Global LTD; Finland; Remote; EUR 90,000 - 130,000; Full time

Job summary

microTECH Global Limited is seeking a passionate researcher to advance state-of-the-art at the intersection of AI and safety. You will design architectures for detection systems that understand multimodal data, enabling robust reasoning over images, videos, text and knowledge graphs.

You will explore visual QA, reasoning, grounding, and neural-symbolic approaches, aiming for interpretable, unbiased and aligned AI. Production-ready tooling and collaboration with stakeholders are expected.

Qualifications

• Ph.D. in Computer Science, Deep Learning, Machine Learning, Mathematics or related fields.
• Strong track record in Explainable AI and Safety domains.
• Proficient with LLMs, neural networks, and computer vision; neuro-symbolic architectures.

Responsibilities

• Design and implement architecture for AI detection systems handling multimodal data.
• Explore visual QA, visual reasoning, text-based reasoning, and grounding using knowledge graphs and neural-symbolic methods.
• Develop interpretable AI techniques, aligned with human values for real-world scenarios.
• Create alignment methods (e.g., RLHF, RLAIF, Constitutional AI) for LLMs, CoT, and agentic workflows.
• Reverse-engineer neural networks to understand internal workings and implement explainability tools.
• Develop data attribution methods to quantify influence of data points on predictions.
• Build benchmarks/datasets to measure reasoning quality, explanations, transparency, and safety treatments.
• Defend against jailbreaking, prompt injections, and adversarial exploits targeting planning and tool-use.

Skills

Explainable AI

Safety

LLMs

Neural networks

Computer vision

Neuro-symbolic

AI alignment

Model robustness

Continual learning

Interpretability

Education

Ph.D. in Computer Science / ML / related field

Tools

Python

Java

C++

PyTorch

TensorFlow

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.