ML Scientist in AI Explainability

ML Scientist in AI Explainability 
Location: Boston Massachusetts
Type: Full time

Machine Learning Scientist, AI Explainability and Scientific Discovery
We are working with a publicly listed deep tech company operating at the intersection of machine learning, material science, and next generation battery technology. The team is applying AI directly to scientific discovery, with real world impact across energy storage, transportation, robotics, and aerospace.

This role sits within an advanced AI research group focused on Large Language Models, AI agents, and explainability in scientific problem solving. Your work will directly influence how new battery materials are discovered and validated using AI.
The position can be fully remote.

What you will work on

• 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.

What we are looking for

• 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.
• Nice to have 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.

What is on offer

• A highly competitive salary and benefits package, including equity in a publicly listed company.
• The chance to work on AI for science problems with visible global impact.
• A collaborative research environment alongside experienced ML scientists, engineers, and domain experts.
• Strong support for professional development, publishing, and long term career growth.