Machine Learning Scientist

Machine Learning Scientist page is loaded## Machine Learning Scientist locations:
San Jose, CAtime type:
Full time posted on:
Posted 26 Days Agojob requisition :
JR100322

Lyten is leading an industrial revolution through Lyten 3D Graphene, a breakthrough supermaterial unlocking a new generation of products — from lithium–sulfur batteries and energy storage systems to concrete admixtures, lightweight composites, and next-generation sensors that are revolutionizing industries. Together, these innovations are making a massive global improvement and driving real-world impact across energy, mobility, construction, and defense.

At Lyten, we believe the most meaningful careers begin with purpose — and with people who want to make a difference. We’re not just developing advanced supermaterials — we’re about to change the world as we know it, reshaping how energy is stored, how products are built, and how progress is made.

We’re entering an exciting growth phase, scaling production across the U.S. and Europe and expanding our team of engineers, scientists, and innovators.

Apply now to join our team and be part of something bigger than yourself — where collaboration, creativity, and purpose come together to build the technologies that will define the next century.## About the Role We are seeking a highly skilled Machine Learning Scientist with expertise in environmental sensing and multivariate sensor data analysis. The ideal candidate will design, implement, and optimize models for detecting, localizing, and visualizing airborne compounds in real-world environments.

This role requires strong technical depth in ML for sensor fusion, as well as experience with computational modeling of dispersion patterns and 3D visualization of concentration fields.## ##

Key Responsibilities
· Develop and deploy ML models for detecting and quantifying airborne compounds from multivariate gas sensor data.
· Design algorithms to estimate concentration gradients, source localization, and spatiotemporal plume dispersion.
· Create 3D visualization tools for mapping gas dispersion and dynamics in the environment, integrating data from a distributed grid of sensors.
· Build scalable systems for real-time sensor data ingestion, preprocessing, and fusion across large sensor arrays.
· Implement physics-informed ML methods (e.g., CFD-informed priors, Gaussian plume models, graph neural networks for spatial grids).
· Collaborate with hardware and embedded systems engineers to ensure ML pipelines are optimized for field deployment.
· Prototype and refine 3D mapping tools that enable end-users to monitor airborne compound plumes as volumetric “cloud maps.”
· Validate models using both simulated and real-world datasets; design experiments to improve detection accuracy and robustness.## ## Qualifications
· Doctorate degree in a relevant field (e.g., chemistry (molecular), materials science, mechanical engineering, electrical engineering, and chemical engineering, computer science)
ORMaster's degree in a relevant field (e.g., chemistry (molecular), materials science, mechanical engineering, electrical engineering, and chemical engineering, computer science) AND 3+ years of experience in a relevant field leading or contributing to multidisciplinary projects where scope requires reliance on the technical experience of other team members
· Strong background in machine learning for time-series and multivariate sensor data.
· Experience with computational fluid dynamics (CFD), plume dispersion models, or environmental modeling.
· Expertise in 3D data visualization (e.g., Unity, WebGL, Three.js, Para View, or similar frameworks).
· Strong proficiency with Python, Tensor Flow/PyTorch, and data visualization libraries.
· Familiarity with distributed sensor networks, IoT data pipelines, and real-time analytics.
· Ability to integrate physics-based models with data-driven ML approaches.
· Track record of publishing, prototyping, or deploying advanced sensing/ML systems.##
· US Citizen or Permanent Resident due to Export Control/ITAR## Preferred Skills
· Experience with Bayesian inference, spatiotemporal statistics, or probabilistic graphical models.
· Knowledge of GIS…