Lyten
We are leading a materials revolution. At Lyten, we’re not just developing advanced supermaterials, we’re about to change the world as we know it. Our team is at the forefront of a fundamental transformation that will impact nearly every industry, making a massive global improvement.
Our mission is to achieve gigaton-scale decarbonization impact, and we’re building a new ecosystem of decarbonization applications to make it a reality. With Lyten 3D Graphene, we’re pushing the boundaries of what’s possible by improving energy storage, developing stronger and lighter plastics, and creating advanced sensors that can detect beyond today’s limits.
Join our team and be a part of something bigger than yourself. We need a diverse set of perspectives and expertise to take on some of the world’s toughest scientific, engineering, and commercial challenges while also having a fun and rewarding career! Are you ready to take on this challenge? Apply now and help us scale Lyten 3D Graphene into all of its many potential applications and be a part of this groundbreaking transformation.
We’re looking for passionate and talented individuals who want to work at Lyten and help create a brighter future for generations to come!
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, materials science, mechanical engineering, electrical engineering, and chemical engineering, computer science) OR Master's degree in a relevant field (e.g., chemistry, 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, ParaView, or similar frameworks). Strong proficiency with Python, TensorFlow/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 systems, spatial data indexing, or large-scale environmental datasets. Familiarity with edge ML deployment (TensorRT, ONNX Runtime, etc.). Strong data storytelling skills and ability to communicate complex results with intuitive visuals. What We Offer
Opportunity to pioneer next-generation environmental sensing systems. Work in a cross-disciplinary team combining embedded systems, cloud architecture, and applied ML. Compensations Range
The expected base salary range for this position is between $155,200.00 - $232,800.00 Disclosures
Pay Transparency Disclosure: This compensation and benefits information is based on Lyten’s estimate as of the date of publication and may be modified in the future. Lyten is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex, age, status as a protected veteran, or status as a qualified individual with a disability. EEO Employer/Vet/Disabled.
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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, materials science, mechanical engineering, electrical engineering, and chemical engineering, computer science) OR Master's degree in a relevant field (e.g., chemistry, 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, ParaView, or similar frameworks). Strong proficiency with Python, TensorFlow/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 systems, spatial data indexing, or large-scale environmental datasets. Familiarity with edge ML deployment (TensorRT, ONNX Runtime, etc.). Strong data storytelling skills and ability to communicate complex results with intuitive visuals. What We Offer
Opportunity to pioneer next-generation environmental sensing systems. Work in a cross-disciplinary team combining embedded systems, cloud architecture, and applied ML. Compensations Range
The expected base salary range for this position is between $155,200.00 - $232,800.00 Disclosures
Pay Transparency Disclosure: This compensation and benefits information is based on Lyten’s estimate as of the date of publication and may be modified in the future. Lyten is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex, age, status as a protected veteran, or status as a qualified individual with a disability. EEO Employer/Vet/Disabled.
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