Senior Applied AI/ML Scientist

NobleAI

Applied AI/ML Scientist developing and deploying machine learning models to solve complex scientific challenges. Collaborating cross-functionally to deliver AI solutions that drive business impact.

Posted 6/23/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $190,000 - $220,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKerasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Design, develop, and deploy machine learning and deep learning models for scientific applications (e.g., materials discovery, chemical modeling, process optimization)
Translate complex scientific and business problems into tractable AI/ML frameworks
Work with real-world structured and unstructured scientific data (e.g., experimental, simulation, and literature data)
Build and maintain data pipelines, feature engineering workflows, and model evaluation frameworks
Collaborate cross-functionally with scientists, engineers, and product managers to deliver production-ready solutions
Partner closely with Customer Success and client-facing teams to understand customer needs, translate requirements into AI/ML solutions, and support the successful deployment, adoption, and ongoing optimization of models in customer environments
Apply techniques such as supervised/unsupervised learning, generative models, and optimization algorithms
Contribute to the integration of models into scalable software platforms and APIs
Stay current with advancements in AI/ML and relevant scientific domains; evaluate and apply new methods where appropriate
Perform strategic research oriented towards improving NobleAI’s core technology
Communicate findings and model outputs clearly to both technical and non-technical stakeholders
Periodic travel to customer sites and attend industry events

Requirements

What you’ll need

Ph.D. in Chemistry, Physics, Biochemistry, Chemical Engineering, or other STEM-related fields
Degree or coursework in Machine Learning
Hands-on experience applying machine learning to real-world problems in science and engineering
Strong background in machine learning: classical and deep learning techniques (examples may include GNNs, transformers, or embedding techniques, etc.)
Strong experience in Python and associated ML frameworks (Pytorch, Tensorflow, Keras, sklearn, etc.)
Demonstrated ability to effectively communicate complex technical details at a high level
Solid understanding of statistical modeling, optimization, and algorithm design
Proven ability to deploy models into production environments
Experience in either representation learning, geometric learning, uncertainty quantification, or unsupervised learning approaches
Experience with cloud or distributed training frameworks (Azure, AWS, GCP) and MLOps practices
Experience in scientific domains such as chemistry, materials science, or physics
Strong familiarity with molecule generation, chemical foundation models, and/or physics-informed ML
Experience with generative AI methods (e.g., diffusion models, flow matching, transformers) applied to scientific problems

Benefits

Comp & perks

Top-tier health benefits coverage, including medical, dental, vision, disability and life insurance
Flexible paid time off & generous holidays
Remote-first with co-working access at Industrious offices
401(k) with employer match
Equity package
Performance-based bonus plan

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdeep learningPythonPytorchTensorflowKerassklearnstatistical modelingoptimization algorithmsgenerative AI methods

Soft Skills

communicationcollaborationproblem-solvingcustomer-focusedresearch-oriented

Certifications

Ph.D. in ChemistryPh.D. in PhysicsPh.D. in BiochemistryPh.D. in Chemical Engineering