We are seeking an Applied AI/ML Scientist that will be responsible for developing and deploying machine learning models that solve complex, real-world scientific and industrial challenges. In this role, you will combine advanced AI/MLapproaches with your energy sector expertise to build models that solve complex industrial problems, optimize processes, and drive real-world breakthroughs. You will work closely with domain experts, product teams, and engineers to translate scientific problems into scalable AI solutions that drive measurable business and customer impact.
If you have a passion for solving industry challenges with real impact, let's talk! Join us in building a more sustainable world through the power of AI and scientific innovation.
Key Responsibilities- 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
RequirementsRequired:- Ph.D. in Geoscience, Geophysics, Production Engineering, Reservoir Engineering, or other Energy-related quantitative discipline
- 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 CNNs, transformers, or embedding techniques, etc.), focus on supervised learning
- 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
Preferred:
- Experience in either Natural Language Processing, Computer Vision, 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 energy
- Familiarity with physics-informed ML, geological/reservoir modeling, and/or production forecasting and flow assurance optimization
- Experience with generative AI methods (e.g., diffusion models, transformers) applied to scientific problems
BenefitsDid we mention we offer great pay & benefits?
- Top-tier health benefits coverage, including medical, dental, vision, disability and life insurance
- Flexible Paid Time Off & generous Holidays
- Remote-first with optional co-working access at The Ion for our Houston-based employees.
- 401(k) with employer match
- Equity package
- Base Salary Range $190k - $220k (Depending on experience & Geographic location)
- Performance-based bonus plan
