DeepRec.ai
Data and Evaluation Applied AI Scientist | ML & R&D Talent | Supporting Startups & Enterprises Across Europe and US
A fast growing energy technology company is expanding its AI research division and is looking for a Data and Evaluation Applied AI Scientist. The team builds advanced AI systems to accelerate discovery in energy storage and next generation battery materials. This role focuses on data correctness, scientific reasoning quality, and the integrity of battery domain knowledge used to train LLMs and multi‑agent scientific frameworks.
The company combines material science and applied AI to develop high energy Li Metal and Li ion battery technologies. Their platform uses AI enhanced discovery to design electrolytes, model electrochemical behavior, and develop new materials with real world applications across transportation, robotics, aerospace, and long‑duration energy storage.
Compensation
– $180k – $310k
What You Will Do
Translate complex battery materials knowledge into structured, high‑quality, AI‑trainable datasets
Lead rigorous data validation, cleaning, and annotation processes to ensure scientific correctness
Create benchmark datasets and design multimodal evaluation suites for model testing
Partner with AI architecture teams to assess and refine model reasoning across chemistry, materials, and electrochemical concepts
Apply techniques that improve correctness and alignment, including methods inspired by human feedback systems
Ensure that AI models demonstrate accurate understanding of molecular behavior, materials interactions, and battery domain logic
Drive the application of battery informatics principles across data pipelines and model development
What You Bring
PhD in Chemical Engineering with a focus on lithium battery systems or in Materials Science or a closely related computational field
Deep domain expertise in battery materials and electrochemical systems
Strong experience converting scientific data into AI‑trainable structures
Practical experience with data validation, annotation, and benchmark dataset creation
Exposure to LLM pipelines, AI evaluation, or multi‑agent scientific workflows
Background in an applied science or AI for materials role
Preferred Experience
Hands‑on experience with RLHF or other model tuning and alignment techniques
Work history in battery labs, materials informatics teams, or AI4Science groups
Experience designing domain‑specific evaluation frameworks for complex AI systems
Experience as a computational battery AI specialist
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The company combines material science and applied AI to develop high energy Li Metal and Li ion battery technologies. Their platform uses AI enhanced discovery to design electrolytes, model electrochemical behavior, and develop new materials with real world applications across transportation, robotics, aerospace, and long‑duration energy storage.
Compensation
– $180k – $310k
What You Will Do
Translate complex battery materials knowledge into structured, high‑quality, AI‑trainable datasets
Lead rigorous data validation, cleaning, and annotation processes to ensure scientific correctness
Create benchmark datasets and design multimodal evaluation suites for model testing
Partner with AI architecture teams to assess and refine model reasoning across chemistry, materials, and electrochemical concepts
Apply techniques that improve correctness and alignment, including methods inspired by human feedback systems
Ensure that AI models demonstrate accurate understanding of molecular behavior, materials interactions, and battery domain logic
Drive the application of battery informatics principles across data pipelines and model development
What You Bring
PhD in Chemical Engineering with a focus on lithium battery systems or in Materials Science or a closely related computational field
Deep domain expertise in battery materials and electrochemical systems
Strong experience converting scientific data into AI‑trainable structures
Practical experience with data validation, annotation, and benchmark dataset creation
Exposure to LLM pipelines, AI evaluation, or multi‑agent scientific workflows
Background in an applied science or AI for materials role
Preferred Experience
Hands‑on experience with RLHF or other model tuning and alignment techniques
Work history in battery labs, materials informatics teams, or AI4Science groups
Experience designing domain‑specific evaluation frameworks for complex AI systems
Experience as a computational battery AI specialist
#J-18808-Ljbffr