Li/Na-Ion Battery Materials Scientist—AI-Driven Lab

Nodi

New York (NY)

On-site

USD 143,000 - 204,000

Full time

8 days ago
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Job summary

Radical AI, based in New York City, is hiring a battery materials scientist to lead Li-ion and Na-ion material development in a robotic, 24/7 lab. You will define the composition space, set synthesis and characterization protocols, and guide the self-driving platform with your electrochemical intuition.

You will design routes for cathodes, anodes, electrolytes, and related compounds, translate results into next rounds, and collaborate with automation and ML teams to embed expertise into

Qualifications

  • PhD in materials science, chemistry, chemical engineering, or related field.
  • Experience synthesizing inorganic materials and characterizing with standard tools (XRD, SEM, electrochemical testing).
  • Comfort in fast-moving, iterative environments with rapid experiment cycles.

Responsibilities

  • Design synthesis routes for battery-relevant materials (cathodes, anodes, electrolytes).
  • Characterize materials using standard techniques and translate results to next-round synthesis decisions.
  • Collaborate with automation and ML teams to encode expertise into self-driving lab protocols.
  • Interpret data and steer the platform on what to explore next.
  • Contribute to the broader science roadmap as the program grows.

Skills

Inorganic synthesis
Materials characterization
Li-ion & Na-ion chemistry
Automation & AI interest

Education

PhD in materials science, chemistry, or chemical engineering

Tools

XRD
SEM
Electrochemical testing

Job description

Radical AI, based in New York City, is hiring a battery materials scientist to lead Li-ion and Na-ion material development in a robotic, 24/7 lab. You will define the composition space, set synthesis and characterization protocols, and guide the self-driving platform with your electrochemical intuition.

You will design routes for cathodes, anodes, electrolytes, and related compounds, translate results into next rounds, and collaborate with automation and ML teams to embed expertise into

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