DeepRec.ai · 13 hours ago
ML Scientist in AI Explainability
DeepRec.ai is a publicly listed deep tech company focused on the intersection of machine learning and scientific discovery. They are seeking an ML Scientist in AI Explainability to lead research into machine learning methods for scientific discovery, particularly in battery and material design, while collaborating with researchers and contributing to publications.
Responsibilities
You will lead research into machine learning methods for scientific discovery, with a strong focus on multimodal Large Language Models and agent based systems
You will study how LLMs reason, plan, and generate solutions when applied to core scientific and engineering questions, particularly in battery and material design
You will design and optimize training pipelines for large models, tackling challenges around data quality, architecture, scalability, and compute efficiency
You will integrate domain specific data sources such as scientific literature and internal research documents into model training and inference
Your research will be deployed into a production multi agent AI system used for real battery technology discovery
You will collaborate closely with researchers, engineers, and external academic labs, and contribute to publications and conference presentations
Qualification
Required
An MSc or PhD in Computer Science, Statistics, Computational Neuroscience, Cognitive Science, or a related field, or equivalent industry experience
Strong grounding in machine learning, deep learning, and Large Language Models, with hands on research experience
Solid Python skills and experience with frameworks such as PyTorch or TensorFlow
Experience working with causal graphs and explainability focused AI methods
A proven research track record, ideally including peer reviewed publications
The ability to explain complex technical ideas clearly to both technical and non technical stakeholders
Preferred
Exposure to AI applied to material science, chemistry, or battery systems
Familiarity with recent research methods in LLM optimization and reinforcement learning approaches such as GRPO
Benefits
Equity in a publicly listed company
Support for professional development
Publishing
Long term career growth